Simulation Using the Affirma Analog Design Environment
simulation modeling and analysis -回复
simulation modeling and analysis -回复Simulation modeling and analysis is a powerful tool used in various industries to understand complex systems, predict their behavior, and make informed decisions. In this article, we will explore what simulation modeling and analysis are, how they work, and why they are valuable in today's world.Simulation modeling is the process of creating a computer-based representation of a real system or process. It involves developing a mathematical model that captures the key components and interactions of the system. This model is then used to simulate the behavior and performance of the system under different scenarios and conditions.Simulation analysis, on the other hand, refers to the process of evaluating the output or results generated by the simulation model. It involves analyzing and interpreting the data produced during the simulation to gain insights into the system's behavior and performance.The first step in simulation modeling and analysis is defining the objectives and scope of the study. This includes identifying the keyvariables, parameters, and constraints that need to be included in the model. For example, in a manufacturing setting, variables such as production rate, inventory levels, and machine downtime may be of interest.Once the objectives and scope are defined, the next step is data collection. This involves gathering relevant data about the system or process under study. This data can come from a variety of sources, including historical records, surveys, and observations. In some cases, it may be necessary to create synthetic or hypothetical data to supplement the available information.After data collection, the model building phase begins. This involves constructing a mathematical representation of the system using specialized software or programming languages. The model should be able to capture the important characteristics and dynamics of the system, such as its inputs, outputs, and interactions.Next, the model needs to be verified and validated. Verification ensures that the model is free from errors and accurately represents the system. Validation, on the other hand, involvescomparing the output of the model with real-world data or expert knowledge to ensure that it accurately captures the system's behavior.Once the model is verified and validated, the simulation experiments can be conducted. These experiments involve running the model using different input values and scenario conditions to generate data on the system's behavior and performance. The output data can then be analyzed using statistical techniques to understand the effects of various factors on the system's performance.Simulation modeling and analysis provide several benefits. First, they allow decision-makers to experiment with different scenarios and conditions without having to disrupt or modify the real system. This can be particularly valuable in sensitive or high-risk environments, where the consequences of change can be costly or dangerous.Second, simulation modeling and analysis provide a level of detail and visibility that is difficult to achieve through other methods. They allow decision-makers to understand the complexinteractions and dependencies within a system, leading to more informed and effective decision-making.Additionally, simulation modeling and analysis can help optimize system performance. By running multiple simulations and analyzing the results, decision-makers can identify bottlenecks, inefficiencies, and areas of improvement. This can lead to cost savings, increased productivity, and enhanced customer satisfaction.In conclusion, simulation modeling and analysis are valuable tools that enable decision-makers to gain insights into complex systems and make informed decisions. By creating a computer-based representation of a system and running simulations,decision-makers can experiment with different scenarios and conditions to understand the system's behavior and optimize its performance. With the increasing complexity of modern systems, simulation modeling and analysis are becoming essential tools in various industries.。
罗姆公司2022年产品用户指南:自动汽车应用Nano Cap 低噪声与输入 输出电压范围高速CMOS
User’s Guide ROHM Solution SimulatorNano Cap™, Low Noise & Input/Output Rail-to-Rail High Speed CMOS Operational Amplifier for Automotive BD7281YG-C – Voltage Follower– Frequency Response simulationThis circuit simulates the frequency response with Op-Amp as a voltage follower. You can observe the AC gain and phase of the ratio of output to input voltage when the input source voltage AC frequency is changed. You can customize the parameters of the components shown in blue, such as VSOURCE, or peripheral components, and simulate the voltage follower with the desired operating condition.You can simulate the circuit in the published application note: Operational amplifier, Comparator (Tutorial). [JP] [EN] [CN] [KR] General CautionsCaution 1: The values from the simulation results are not guaranteed. Please use these results as a guide for your design.Caution 2: These model characteristics are specifically at Ta=25°C. Thus, the simulation result with temperature variances may significantly differ from the result with the one done at actual application board (actual measurement).Caution 3: Please refer to the Application note of Op-Amps for details of the technical information.Caution 4: The characteristics may change depending on the actual board design and ROHM strongly recommend to double check those characteristics with actual board where the chips will be mounted on.1 Simulation SchematicFigure 1. Simulation Schematic2 How to simulateThe simulation settings, such as parameter sweep or convergence options,are configurable from the ‘Simulation Settings’ shown in Figure 2, and Table1 shows the default setup of the simulation.In case of simulation convergence issue, you can change advancedoptions to solve. The temperature is set to 27 °C in the default statement in‘Manual Options’. You can modify it.Figure 2. Simulation Settings and execution Table 1.Simulation settings default setupParameters Default NoteSimulation Type Frequency-Domain Do not change Simulation TypeStart Frequency 10 Hz Simulate the frequency response for thefrequency range from 10 Hz to 100 MHz.End Frequency 100Meg HzAdvanced options More Accuracy - Time Resolution Enhancement Convergence Assist-Manual Options .temp 27 - SimulationSettingsSimulate3 Simulation Conditions4 Op-Amp modelTable 3 shows the model pin function implemented. Note that the Op-Amp model is the behavior model for its input/output characteristics, and no protection circuits or the functions not related to the purpose are not implemented.5 Peripheral Components5.1 Bill of MaterialTable 4 shows the list of components used in the simulation schematic. Each of the capacitors has the parameters of equivalent circuit shown below. The default values of equivalent components are set to zero except for the ESR ofC. You can modify the values of each component.Table 4. List of capacitors used in the simulation circuitType Instance Name Default Value Variable RangeUnits Min MaxResistor R1_1 0 0 10 kΩRL1 10k 1k 1M, NC ΩCapacitor C1_1 0.1 0.1 22 pF CL1 25 free, NC pF5.2 Capacitor Equivalent Circuits(a) Property editor (b) Equivalent circuitFigure 3. Capacitor property editor and equivalent circuitThe default value of ESR is 0.01 Ω.(Note 2) These parameters can take any positive value or zero in simulation but it does not guarantee the operation of the IC in any condition. Refer to the datasheet to determine adequate value of parameters.6 Recommended Products6.1 Op-AmpBD7281YG-C : Nano Cap™, Low Noise & Input/Output Rail-to-Rail High Speed CMOS Operational Amplifier for Automotive. [JP] [EN] [CN] [KR] [TW] [DE]TLR4377YFV-C : Automotive High Precision & Input/Output Rail-to-Rail CMOS Operational Amplifier (QuadOp-Amp). [JP] [EN] [CN] [KR] [TW] [DE]TLR2377YFVM-C : Automotive High Precision & Input/Output Rail-to-Rail CMOS Operational Amplifier (DualOp-Amp). [JP] [EN] [CN] [KR] [TW] [DE]TLR377YG-C : Automotive High Precision & Input/Output Rail-to-Rail CMOS Operational Amplifier. [JP] [EN] [CN] [KR] [TW] [DE]LMR1802G-LB : Low Noise, Low Input Offset Voltage CMOS Operational Amplifier. [JP] [EN] [CN] [KR] [TW] [DE] Technical Articles and Tools can be found in the Design Resources on the product web page.NoticeROHM Customer Support System/contact/Thank you for your accessing to ROHM product informations.More detail product informations and catalogs are available, please contact us.N o t e sThe information contained herein is subject to change without notice.Before you use our Products, please contact our sales representative and verify the latest specifica-tions :Although ROHM is continuously working to improve product reliability and quality, semicon-ductors can break down and malfunction due to various factors.Therefore, in order to prevent personal injury or fire arising from failure, please take safety measures such as complying with the derating characteristics, implementing redundant and fire prevention designs, and utilizing backups and fail-safe procedures. ROHM shall have no responsibility for any damages arising out of the use of our Poducts beyond the rating specified by ROHM.Examples of application circuits, circuit constants and any other information contained herein areprovided only to illustrate the standard usage and operations of the Products. The peripheral conditions must be taken into account when designing circuits for mass production.The technical information specified herein is intended only to show the typical functions of andexamples of application circuits for the Products. ROHM does not grant you, explicitly or implicitly, any license to use or exercise intellectual property or other rights held by ROHM or any other parties. ROHM shall have no responsibility whatsoever for any dispute arising out of the use of such technical information.The Products specified in this document are not designed to be radiation tolerant.For use of our Products in applications requiring a high degree of reliability (as exemplifiedbelow), please contact and consult with a ROHM representative : transportation equipment (i.e. cars, ships, trains), primary communication equipment, traffic lights, fire/crime prevention, safety equipment, medical systems, servers, solar cells, and power transmission systems.Do not use our Products in applications requiring extremely high reliability, such as aerospaceequipment, nuclear power control systems, and submarine repeaters.ROHM shall have no responsibility for any damages or injury arising from non-compliance withthe recommended usage conditions and specifications contained herein.ROHM has used reasonable care to ensur e the accuracy of the information contained in thisdocument. However, ROHM does not warrants that such information is error-free, and ROHM shall have no responsibility for any damages arising from any inaccuracy or misprint of such information.Please use the Products in accordance with any applicable environmental laws and regulations,such as the RoHS Directive. For more details, including RoHS compatibility, please contact a ROHM sales office. ROHM shall have no responsibility for any damages or losses resulting non-compliance with any applicable laws or regulations.W hen providing our Products and technologies contained in this document to other countries,you must abide by the procedures and provisions stipulated in all applicable export laws and regulations, including without limitation the US Export Administration Regulations and the Foreign Exchange and Foreign Trade Act.This document, in part or in whole, may not be reprinted or reproduced without prior consent ofROHM.1) 2)3)4)5)6)7)8)9)10)11)12)13)。
论文中的simulation和emulation
论⽂中的simulation和emulation如题,作为⼀名学术研究者,关于simulation和emulation是有必要分清楚的。
先给出⼀些⽹上的参考定义:解释⼀:模拟(Simulation)即选取⼀个物理的或抽象的系统的某些⾏为特征,⽤另⼀系统来表⽰它们的过程。
模拟技术的⾼级阶段称为仿真模拟(Emulation)、系统仿真,即⽤⼀数据处理系统来全部或部分地模拟某⼀数据处理系统,以致于模仿的系统能想被模仿的系统⼀样接受同样的数据、执⾏同样的程序、获得同样的结果。
解释⼆:模拟(Emulation)是试图模仿⼀个设备的内部设计;仿真(Simulation)是试图模仿⼀个设备的功能。
解释三: Emulation:When one system performs in exactly the same way as another, though perhaps not at the same speed. A typical example would be emulation of one computer by ( a program running on) another. You migh use emulation as a replacement for a system whereas you would use a simulation if you just wanted to analyse it and make predictions about it. Simulation: Attempting to predict aspects of the behaviour of some system by creating an approximate (mathematical) model of it. This can be done by physical modelling, by writing a special-purpose computer program or using a more general simulation package, probably still aimed at a particular kind of simulation (e.g. structural engineering, fluid flow). Typical examples are aricraft flight simulators or electronic circuit simulators. A great many simulation languages exist, e.g. {Simula}总结下来就是simulation是模拟,emulation是仿真。
spectre_tut
Should use parameter definitions for constant values – makes it easier to experiment with different values.
BR 8/04
9
Subcircuit Definitions
// lmin, wmin defined in model file can experiment with subckt NAND2X1 A B Y vddc gndc M3 (Y A net29 gndc) N_def w=2*wmin l=lmin different technologies. M4 (net29 B gndc gndc) N_def w=2*wmin l=lmin M2 (Y B vddc vddc) P_def w=2*wmin l=lmin M1 (Y A vddc vddc) P_def w=2*wmin l=lmin ends NAND2X1 subckt INVX1 A Y vddc gndc M2 (Y A gndc gndc) N_def w=wmin l=lmin M1 (Y A vddc vddc) P_def w=2*wmin l=lmin ends INVX1 subckt INVX4 A Y vddc gndc M2 (Y A gndc gndc) N_def w=wmin*4 l=lmin M1 (Y A vddc vddc) P_def w=2*wmin*4 l=lmin ends INVX4
•
delta_probe.def is a Spectre HDL model that implements a probe for measuring delay between two events
PSpice仿真软件使用指南说明书
April 2016© 2013Cadence Design Systems, Inc. All rights reserved.Portions © Apache Software Foundation, Sun Microsystems, Free Software Foundation, Inc., Regents of the University of California, Massachusetts Institute of T echnology, University of Florida. Used by permission. Printed in the United States of America.Cadence Design Systems, Inc. (Cadence), 2655 Seely Ave., San Jose, CA 95134, USA.Product PSpice contains technology licensed from, and copyrighted by: Apache Software Foundation, 1901 Munsey Drive Forest Hill, MD 21050, USA © 2000-2005,Apache Software Foundation. Sun Microsystems, 4150 Network Circle, Santa Clara, CA 95054 USA © 1994-2007, Sun Microsystems, Inc. Free Software Foundation, 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA © 1989, 1991, Free Software Foundation, Inc. Regents of the University of California, Sun Microsystems, Inc., Scriptics Corporation, © 2001, Regents of the University of California. Daniel Stenberg, © 1996 - 2006, Daniel Stenberg. 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Except as may be explicitly set forth in such agreement, Cadence does not make, and expressly disclaims, any representations or warranties as to the completeness, accuracy or usefulness of the information contained in this document. Cadence does not warrant that use of such information will not infringe any third party rights, nor does Cadence assume any liability for damages or costs of any kind that may result from use of such information.Restricted Rights: Use, duplication, or disclosure by the Government is subject to restrictions as set forth in FAR52.227-14 and DFAR252.227-7013 et seq. or its successor.ContentsBefore you begin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Welcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 How to use this guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Symbols and conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Related documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 What this user’s guide covers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 PSpice overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Add-on options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 PSpice Smoke Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 PSpice Advanced Optimizer Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 PSpice Advanced Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 SLPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 If you don’t have the standard PSpice A/D package . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Comparison of the different versions of PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 If you have PSpice Lite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Minimum hardware requirements for running PSpice: . . . . . . . . . . . . . . . . . . . . . . . . 32 PSpice Samples and T utorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Part one: Simulation primer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 1Things you need to know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 What is PSpice? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Analyses you can run with PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Basic analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Advanced multi-run analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Analyzing waveforms with PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 What is waveform analysis? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Using PSpice with other programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Using design entry tools to prepare for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 47What is the PSpice Stimulus Editor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 What is the PSpice Model Editor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Files needed for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Files that design entry tool generates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Other files that you can configure for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Files that PSpice generates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Directory structure for analog projects in Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 How are files configured at the design level maintained in the directory structure for analog projects? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 How are files configured at the profile level maintained in the new directory structure for analog projects? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 What happens when I convert an analog project that uses a design from another project or from another location? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 What should I do if the schematic for a converted analog project uses FILESTIM n parts from the SOURCE library? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Design Entry HDL libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Reference Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Local libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 PSpice model libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 The cds.lib file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Encrypting PSpice Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Using PSpiceEnc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Using Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722Simulation examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Example circuit creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Using Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Using Design Entry HDL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Using Design T emplates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Finding out more about setting up your design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Running PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Performing a bias point analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Using the simulation output file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Finding out more about bias point calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99DC sweep analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Setting up and running a DC sweep analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Displaying DC analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Finding out more about DC sweep analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 T ransient analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Finding out more about transient analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 AC sweep analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Setting up and running an AC sweep analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 AC sweep analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Finding out more about AC sweep and noise analysis . . . . . . . . . . . . . . . . . . . . . . . 122 Parametric analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Setting up and running the parametric analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Analyzing waveform families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Finding out more about parametric analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Finding out more about performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Part two: Design entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1383Preparing a design for simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Checklist for simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 T ypical simulation setup steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Advanced design entry and simulation setup steps . . . . . . . . . . . . . . . . . . . . . . . . . 141 When netlisting fails or the simulation does not start . . . . . . . . . . . . . . . . . . . . . . . . 142 Using parts that you can simulate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Vendor-supplied parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Passive parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Breakout parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Behavioral parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Simulating asymmetric parts in PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Simulating homogenous parts in PSpice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Specifying values for part properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Using global parameters and expressions for values . . . . . . . . . . . . . . . . . . . . . . . . . . 158Global parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Defining power supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 For the analog portion of your circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 For A/D interfaces in mixed-signal circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Defining stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Analog stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Digital stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Things to watch for . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Unmodeled parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Unconfigured model, stimulus, or include files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Unmodeled pins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Missing ground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Missing DC path to ground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1854Creating and editing models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 What are models? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 How are models organized? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Model libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Model library configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Global vs. design vs. profile models and libraries . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Nested model libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 PSpice-provided models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Model library data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Device characteristic curves-based models vs. Template-based models . . . . . . . . 195 T ools to create and edit models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Ways to create and edit models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Using the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Ways to use the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Running the Model Editor alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Starting the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Creating models using the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Creating models based on device characteristic curves . . . . . . . . . . . . . . . . . . . . . 203Creating models based on PSpice templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Importing an existing model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Enabling and disabling automatic part creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Running the Model Editor from the schematic editor . . . . . . . . . . . . . . . . . . . . . . . . 215 Model creation examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 Example: Creating a PSpice model based on device characteristic curves . . . . . . . 219 Example: Creating template-based PSpice model . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Editing model text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Example: editing a Q2N2222 instance model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Using the Create Subcircuit Format Netlist command (Capture only) . . . . . . . . . . . . . . 237 Changing the model reference to an existing model definition . . . . . . . . . . . . . . . . . . . 239 Reusing instance models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Reusing instance models in the same schematic . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Making instance models available to all designs . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Configuring model libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 The Configuration Files tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 How PSpice uses model libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Adding model libraries to the configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Changing the model library scope from profile to design, profile to global, design to global and vice versa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Changing model library search order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Changing the library search path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 Handling smoke information using the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Adding smoke information to PSpice models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Creating template-based PSpice models with smoke information . . . . . . . . . . . . . . 256 Using the Model Editor to edit smoke information . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Examples: Smoke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Adding smoke information to the D1 diode model . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Adding smoke information to the OPA_LOCAL operational amplifier model . . . . . . 259 Smoke parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Diode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Bipolar Junction Transistors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Magnetic Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Ins Gate Bipolar T ransistor (IGBT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Junction FET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Operational Amplifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Voltage Regulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Darlington T ransistor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2735Creating parts for models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 What’s different about parts used for simulation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Ways to create parts for models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Preparing your models for part creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Starting the Model Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Using the Model Editor to create parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Batch mode of part creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Interactive mode of part creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Creating Design Entry T ool parts for all models in a library . . . . . . . . . . . . . . . . . . . . . . 282 Using batch mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 Using interactive mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Setting up automatic part creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Creating parts in the batch mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Creating parts using interactive mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Basing new parts on a custom set of parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Editing part graphics (Capture only) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 How Capture places parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Defining grid spacing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Attaching models to parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Defining part properties needed for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 PSPICETEMPLATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 IO_LEVEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 MNTYMXDL Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 PSPICEDEFAULTNET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3216Analog behavioral modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Overview of analog behavioral modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 The ABM part library file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Placing and specifying ABM parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 Net names and device names in ABM expressions . . . . . . . . . . . . . . . . . . . . . . . . . 326 Forcing the use of a global definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 ABM part templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 Control system parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Basic components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 Limiters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Chebyshev filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Integrator and differentiator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 T able look-up parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Laplace transform part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Math functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 ABM expression parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 An instantaneous device example: modeling a triode . . . . . . . . . . . . . . . . . . . . . . . 353 PSpice-equivalent parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Implementation of PSpice-equivalent parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Modeling mathematical or instantaneous relationships . . . . . . . . . . . . . . . . . . . . . . 358 Lookup tables (ET ABLE and GT ABLE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 Frequency-domain device models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Laplace transforms (LAPLACE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Frequency response tables (EFREQ and GFREQ) . . . . . . . . . . . . . . . . . . . . . . . . . 366 Cautions and recommendations for simulation and analysis . . . . . . . . . . . . . . . . . . . . . 369 Instantaneous device modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Frequency-domain parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Laplace transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 T rading off computer resources for accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Basic controlled sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Creating custom ABM parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375。
Mix signal simulation flow解读
Hale Waihona Puke 华虹设计9GUI:
DUT Import
A D B C
B
V
Confidential and Proprietary
华虹设计 10
GUI: sdf annotation
Solution 1: add following command to verilog stimulus file.
华虹设计 23
GUI: Sim-Mode select
Confidential and Proprietary
华虹设计 24
GUI: ADE Simulation Setup
Choose Design/Simulator/Directory
Confidential and Proprietary
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电子工作台TM多模式TM9模拟和捕捉组件参考指南说明书
Electronics Workbench TMMultisim 9 Simulation and Capture Component Reference GuideFebruary 2006371587B-01Worldwide Technical Support and Product InformationNational Instruments Corporate Headquarters11500 North Mopac Expressway Austin, Texas 78759-3504USA Tel: 512 683 0100Worldwide OfficesAustralia1800300800, Austria4306624579900, Belgium32027570020, Brazil551132623599,Canada8004333488, China862165557838, Czech Republic420224235774, Denmark4545762600, Finland3850972572511, France330148142424, Germany490897413130, India918041190000,Israel972036393737, Italy3902413091, Japan81354722970, Korea820234513400,Lebanon96101332828, Malaysia1800887710, Mexico018000100793, Netherlands310348433466,New Zealand0800553322, Norway47066907660, Poland48223390150, Portugal351210311210,Russia70957836851, Singapore180********, Slovenia38634254200, South Africa270118058197, Spain34916400085, Sweden460858789500, Switzerland41562005151, Taiwan8860223772222, Thailand6622786777, United Kingdom4401635523545For further support information, refer to the Technical Support Resources and Professional Services page. 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BECAUSE EACH END-USER SYSTEM IS CUSTOMIZED AND DIFFERS FROM NATIONAL INSTRUMENTS' TESTING PLATFORMS AND BECAUSE A USER OR APPLICATION DESIGNER MAY USE NATIONAL INSTRUMENTS PRODUCTS IN COMBINATION WITH OTHER PRODUCTS IN A MANNER NOT EVALUATED OR CONTEMPLATED BY NATIONAL INSTRUMENTS, THE USER OR APPLICATION DESIGNER IS ULTIMATELY RESPONSIBLE FOR VERIFYING AND VALIDATING THE SUITABILITY OF NATIONAL INSTRUMENTS PRODUCTS WHENEVER NATIONAL INSTRUMENTS PRODUCTS ARE INCORPORATED IN A SYSTEM OR APPLICATION, INCLUDING, WITHOUT LIMITATION, THE APPROPRIATE DESIGN, PROCESS AND SAFETY LEVEL OF SUCH SYSTEM OR APPLICATION.Component Reference GuideThis guide contains information on the components found in Multisim 9.The chapters in the Component Reference Guide are organized to follow the component groups that are found in the Multisim 9 databases.License AgreementPlease read the license agreement found at carefully before installing and using the software contained in this package. By installing and using the software, you are agreeing to be bound by the terms of this license. If you do not agree to the terms of this license, simply return the unused software within ten days to the place where you obtained it and your money will be refunded.Table of Contents1. Source Components1.1Ground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11.1.1About Grounding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11.1.2The Ground Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-21.2Digital Ground. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-21.3DC Voltage Source (Battery) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-21.3.1Battery Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-21.3.2Battery Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-31.4VCC Voltage Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-31.5DC Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-41.6AC Voltage Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-41.7AC Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-51.8Clock Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-51.9Amplitude Modulation (AM) Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-51.9.1Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-61.10FM Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-61.10.1FM Voltage Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-61.10.2Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-71.10.3FM Current Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-71.10.4Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-71.11FSK Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-81.12Voltage-Controlled Voltage Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-91.13Current-Controlled Voltage Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-91.14Voltage-Controlled Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-101.15Current-Controlled Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-101.16Voltage-Controlled Sine Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-111.16.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-111.16.2Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11 Multisim 9 Component Reference Guide i1.17Voltage-Controlled Square Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-131.17.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-131.17.2Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-131.18Voltage-Controlled Triangle Wave. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-151.18.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-151.18.2Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-151.19Voltage-Controlled Piecewise Linear Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-161.20Voltage Controlled Resistor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-171.21 Piecewise Linear Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-181.21.1PWL Source Input Text File Specification . . . . . . . . . . . . . . . . . . . . . . . . .1-181.21.2Piecewise Linear Voltage Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-201.21.3Piecewise Linear Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-201.22Pulse Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-211.22.1Pulse Voltage Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-221.22.2Pulse Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-231.23Polynomial Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-231.23.1 Output Voltage Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . .1-241.24Exponential Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-241.24.1Exponential Voltage Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-251.24.2Exponential Current Source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-261.25Nonlinear Dependent Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-261.26Controlled One-Shot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-271.27Magnetic Flux Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-281.28Magnetic Flux Generator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-281.29Multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-291.29.1Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-311.29.2Multiplier Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-311.30Divider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-311.30.1Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-331.30.2Divider Parameters and Defaults. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-331.31Transfer Function Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-341.31.1Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-341.31.2Transfer Function Block Parameters and Defaults . . . . . . . . . . . . . . . . . .1-35 ii Electronics Workbench1.32Voltage Gain Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-361.32.1Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-371.32.2Voltage Gain Block Parameters and Defaults. . . . . . . . . . . . . . . . . . . . . . 1-371.33Voltage Differentiator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-381.33.1Investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-381.33.1.1Sine wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-381.33.1.2Triangle waveforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-381.33.1.3Square waves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-391.33.2Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-401.33.3Voltage Differentiator Parameters and Defaults . . . . . . . . . . . . . . . . . . . . 1-401.34Voltage Integrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-401.34.1Investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-411.34.2Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-411.34.3Voltage Integrator Parameters and Defaults. . . . . . . . . . . . . . . . . . . . . . . 1-421.35Voltage Hysteresis Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-421.35.1Hysteresis Block Parameters and Defaults. . . . . . . . . . . . . . . . . . . . . . . . 1-431.36Voltage Limiter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-441.36.1Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-451.36.2Voltage Limiter Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . 1-451.37Current Limiter Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-451.37.1Current Limiter Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . 1-471.38Voltage-Controlled Limiter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-481.38.1Voltage-Controlled Limiter Parameters and Defaults . . . . . . . . . . . . . . . . 1-491.39Voltage Slew Rate Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-501.39.1Voltage Slew Rate Block Parameters and Defaults . . . . . . . . . . . . . . . . . 1-511.40Three-Way Voltage Summer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-521.40.1Charactistic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-531.40.2Summer Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-541.41Three Phase Delta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-541.42Three Phase Wye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-541.43Thermal Noise Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-551.44TDM Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-551.45LVM Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-56 Multisim 9 Component Reference Guide iii2. Basic Components2.1Connectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-12.2Rated Virtual Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-12.3Sockets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-22.4Switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-22.4.1Switch Packs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-32.5Resistor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-42.5.1Resistor: Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-52.5.2About Resistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-52.5.3Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-52.5.4Virtual Resistor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-62.6Capacitor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-62.6.1Capacitor: Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-72.6.2Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-72.6.3DC Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-72.6.3.1Time-Domain Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-72.6.4AC Frequency Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-82.6.5Capacitor Virtual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-92.7 Inductor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-92.7.1Inductor: Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-92.7.2Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-102.7.3DC Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-102.7.4Time-Domain Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-102.7.5AC Frequency Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-112.7.6Inductor Virtual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-112.8Transformer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-122.8.1Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-122.8.2Ideal Transformer Model Parameters and Defaults. . . . . . . . . . . . . . . . . .2-132.9Nonlinear Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-132.9.1Customizing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-132.9.2Nonlinear Transformer Parameters and Defaults . . . . . . . . . . . . . . . . . . .2-142.10Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-152.10.1Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-152.10.2Characteristic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-16 iv Electronics Workbench2.11Variable Capacitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-162.11.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-162.11.2Characteristic Equation and Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-172.11.3Virtual Variable Capacitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-172.12Variable Inductor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-172.12.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-172.12.2Characteristic Equation and Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-172.12.3Virtual Variable Inductor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-182.13Potentiometer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-182.13.1The Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-182.13.2Characteristic Equation and Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-192.13.3Virtual Potentiometer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-192.14Pullup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-192.15Resistor Packs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-192.16Magnetic Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-202.16.1Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-202.16.2Magnetic Core Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . 2-212.17Coreless Coil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-212.17.1Characteristic Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-222.17.2Coreless Coil Parameters and Defaults . . . . . . . . . . . . . . . . . . . . . . . . . . 2-222.18Z Loads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-222.18.1A+jB Block. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-222.18.2A-jB Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-232.18.3Z Load 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-232.18.4Z Load 1 Delta. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-232.18.5Z Load 1 Wye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-242.18.6Z Load 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-242.18.7Z Load 2 Delta. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-242.18.8Z Load 2 Wye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-252.18.9Z Load 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-253. Diodes3.1Diode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13.1.1Diodes: Background Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13.1.2DC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23.1.3Time-Domain Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-33.1.4AC Small-Signal Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 Multisim 9 Component Reference Guide v。
Cadence电路参数变量扫描分析说明
Cadence 电路参数变量扫描分析庞则桂 2006-12-22 Version-1.0在使用 Cadence 的 Affirma Analog Circuit Design Environment 对电路进行仿真的时候, 适当地使用 Design Variables 将会获得事半功倍的效果。
什么是 Design Variables?直观地说它们就是出现在 Affirma Analog Circuit Design Environment 界面中的一些可变的参数值。
如下图红色虚线框中所示的就是一些已经定义好 了的变量。
图 1 Affirma Analog Circuit Design Environment 界面变量可以是一些全局的参数。
例如 temp,就是系统默认的温度参数,当设置对温度变 量进行扫描分析时,可以获得一组代表了整个电路在不同温度下某个参数的变化曲线。
变量还可以是电路中某些元器件的具体数值。
通过扫描该参数可以获得该器件的这个参 数值在一定范围内变化对整个电路的影响。
Cadence 还可以对多个变量进行扫描,可以获得 电路多个参数同时变化时的最优值, 这对设计电路, 确定元器件的取值具有非常重要的作用。
变量的取值可以是具体的数值、等式或者表达式。
这里我们主要讨论数值的情况,关于 等式和表达式的变量取值还没有用到,以后接触到之后再继续详述之。
对变量进行扫描,例如电路中激励源 vdc 的直流电压大小,电路中某个电容,电阻的大 小等等,使用 Analyses 菜单下的 Choose dc,即 DC sweep(直流参数扫描)也可以分析电 路某个参数变化对整体的影响,为什么还要刻意地进行 Design Variables 的参数变量扫描分 析呢?两者有什么不同?我们可以先假设这样一种情况。
假如, 我有一个电路, 在上电之后, 要延时一定的时间电路才能开始正常工作,而且这个启动的过程不可忽略,那么,我就需要 进行时域上的分析,即采用瞬态分析(tran)才能仿真出这个过程。
spectresimulation介绍PPT教案
3、View则包含多种类型,常用的有schemetic,symbol, layout,extracted,ivpcell等等 ,新建Cellview要注意选择 View的类型。
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Tools菜单
在Tools菜单下,比较常 用的菜单项有
Library Manager Library Path Editor Technology File Manager
Library Manager项打开的是库管 理器。在窗口的各部分中,分别 显示的是Library、Category、Cell 、View相应的内容。
i
11.Wire(Narrow) w
12.Wire(Wide)
13.Wire Name
l
14.Pin
p
15.Cmd Options
16.Repeat
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添加元器件
点击右边工具栏“Instance”或快捷键“I”
基本的元器件, 如NMOS PMOS 电阻 电容 电压源 电流源 等等 都在 analoglib库里。
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ac(交流分析)
ac(交流分析)是 分析电路性能随着 运行频率变化而变
化的仿真。
既可以对频率进行 扫描也可以在某个 频率下进行对其它
变量的扫描。
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Variables菜单
包括Edit等子菜单项。可以 对变量进行添加、删除、 查找、复制等操作。变量 (variables)既可以是电 路中元器件的某一个参量 ,也可以是一个表达式。 变量将在参量扫描( parametric analysis)时用 到。
Simulation of LADAR and FLIR data. Second quarter report.
Simulation of LADAR and FLIR data.Second quarter report.Gavin Powell10April19991IntroductionRecent advances in laser technology have meant that laser radar has now become a viable option where before it was thought that it was too expensive or the data that it provided was of poor quality.It’s uses in vision are obvious as it can directly obtain the three-dimensional data of the object it is scanning.Even though the technology has improved and become cheaper the actual acquisition can be expensive due to the very nature of ATR scenarios leading to a lack of readily available data.The need for an easier method of obtaining data for use in testing ATR systems is evident and the obvious way forward is to design software that mimics the sensors to give an accurate simulation of real data.This also has advantages over testing systems analytically as it allows ATR systems to be tested with data that is very similar to real data.The modelling techniques that we use also allow us to look at nearly any object,in any pose,in high detail.We are designing a piece of software that will be able to simulate LADAR and FLIR data for the testing of our proposed Air-To-Air ATR system.The very fact that the system is intended for Air-To-Air scenarios allows us to make some assumptions and simplifications when compared with systes which are designed for other scenarios.Some techniques used by Bevington[1]that have been developed for air-to-ground LADAR systems have included ways of simulating objects that are partially occluded by solid objects or vegetation that only allow a portion of the beam to hit the target.For our system we assume that there is no partial occlusion occruring within our blue sky environment and use a frequency modulated ladar sensor simulation technique.The FLIR simulation is a simultaneous process within the software that models surface heat due to heat flux from the sun and also heatflow though the model that has a heat source within it,such as an engine.We use a simplified model that treats the heat source as being a point source and theflow of heat through the object as being one dimensional.We feel that for our system this is sufficient but also leaves room for extra features as and when they are necessary.2FLIR Image SimulationThe simulation of our infrared images have two distinct factors that add to the surface temperature of the object in question.Firstly the temperature due to heatflow from the sun is calculated and then the heatflow from the point heat source within the object is calculated and combined to give overall temperature for the FLIR.2.1Heatflow due to internal heat sourceWe simplify the model by assuming one dimensional heatflow within the object and that the heat source is a point source.The basic equation for one dimensional heat conduction within an object is given by Fourier’sLawq′′x=k T2−T1L(1)where q′′x is the heatflux,k is the thermal conductivity,T is the temperature and L is distance.2.2Heatflow due to solar radiationIt can be assumed that heatflow into the object equals heatflow from the object so the net heatflow at a surface of the object is given byW abs=W cv+W rad+W bal(2) where W abs is the heatflux absorbed,W cv is convectional heatflux loss at the surface and W bal is heat lostby other processes.W abs=αs W s cosθwhere W s=Aexp(B/sin a)(3)Where W s is the solar radiation of a surface whose normal is paral;lel to the sun’s rays,θis the angle made by the sun’s rays,a is the angular altitude of the sun andαis the solar absorbtivity of the surface.The values of A and B are parameters for the time of year that are related to position of the object on the earth with respect to the sun and can be found in[7].The net heatflux lost to the surrounding atmosphere due to radiation is given byW rad=ǫ0σ(T4s−T4amb)(4) which is the Stefan-Boltzman law where T s is the absolute surface temperature,T air is the absolute tempera-ture of the surrounding air,ǫ0is the emissivity of the material andσis the Stefan-Boltzman constant.Heatflux lost to the surroundings by convection is given byW cv=h(T s−T air)(5) h being the heat transfer coefficient.The value of h is dependant on various properties of the surrounding air including its speed relative to the object.The various values for h are documented in[8].R=W absW bal(6)Nandhakumar and Aggarwal have evaluated R in[5].Substituting into equation(3)we getσǫ0T4s+hT s+(R−1)W sαs cosθ−hT air−σǫ0T4air=0(7) The roots to this equation were found using Laguerre’s method taken from[9],and it was found that two are complex,one is negative and one is real.We can ignore the complex values and as we are working in absolute temperatures we can ignore negative roots.It is extremely dificult to model heatflux for surfaces that are inshadow.A surface is in shadow ifθ≥π2,when this is the case the third term becomes zero and forces theequation to be T s=T air so we assume that surface is not heated by the sun.2.3Temperature of surfaceWe now have two elements providing a temperature change at the surface through equations(1)and(3).So the surface temperature is now given byT=T sun+T point(8) 3FM LADAR image simulationThe FM laser technique creates a pulse signal where the frequency increases or decreases linearly over the duration of the pulse.A portion of the transmit laser output is used as the local oscillator source.A signal returned from a stationary target produces a sinusoidal current at the detector output at a frequency that is proportional to target range.A Fourier analysis of this current then provides a profile of reflected signal intensity as a function of range.So we have two currents coming into the optical detector,a sinusoidal one-1[s o+(υ)+s o+(−υ)].(9) s o+(υ)=A sys1rΓexp(−j2πτυ)h(υ−(f d∓τB F M/T p)).(10)What we have here ish(υ−(f d∓τB F M/T p))(11) this is the amplitude of the Fourier transform of the local oscillator at frequencyυ.This also contains the up and down sweep for the frequency that eradicates any doppler shift caused by radial motion of the target.The other part of the equation.A sys 1rΓexp(−j2πτυ)(12)gives the signal return from the surface broken down into its relative sinusoidal and cosinosoidal componenets at frequencyυ.So this can be thought of as the Fourier transform of the returned signal from the surface. Multiplying these two together is like convolution which would mean that it is analysing how the pulse wave ineracts with the returned waveform when convolved they produce the signal output for frequencyυ.Which would make sense as the pulse waveform is the original transmitted waveform and the returned signal waveform is relative to the range of the target so comparing the two will allow us tofind the range.3.1Our MethodologyWe take a rectangular pulse wave form andfind the Discrete Fourier Transform.F(n)=N−1k=0h k exp(−j2πkn/N)(13)for n=0,1,···,N-1.We now have a set of points in Fourier space which are symetrical about N when we plot |F(υ)|= Re2+Imag2(seefigure1)Figure1:DFT of a square wave with no zero padding10N ∆where n =−N 2,···,N 2(14)Where N ∆is the duration of the pulse,T p .Now we are given the bandwidth,B F M ,of the pulse envelope function so υat N =B F M .So we can either sample discretely at a rate that gives us the required max and min frequencies or we can interpolate between the frequencies to get better definition without increasing the bandwidth by zero padding the the data sequence with L-N zeros.(see figure 2).Figure 2:DFT of a square wave with zero paddingThe frequency and amplitude values obtained from the DFT of the square wave are placed into a look-up table for future reference by the system.From equation (10)A sys 1r Γwill remain constant for each individual ray soC 1=A sys 1Γ(15)also taking the real and imaginary parts of equation (11)let(C Re 2+jC Imag 2)=h (υ−(f o ∓τB F M /T p ))(16)(C Re 3+jC Imag 3)=h (−υ−(f o ∓τB F M /T p ))(17)so substituting the real and imaginary constants into equation (10)we gets o +(υ)=C 1exp (−j 2πτυ)(C Re 2+jC Imag 2)(18)=C 1(C Re 2+jC Imag 2)(cos(2πτυ)−j sin(2πτυ))=C 1(C Re 2+jC Imag 2)cos(2πτυ)−C 1(C Re 2+jC Imag 2)j sin(2πτυ)=[C 1C Re 2cos(2πτυ)+C 1jC Imag 2cos(2πτυ)]−[C 1C Re 2j sin(2πτυ)−C 1C Imag 2sin(2πτυ)]Re s o +(υ)=C 1[C Re 2cos(2πτυ)+C Imag 2sin(2πτυ)](19)Imag s o +(υ)=jC 1[C Imag 2cos(2πτυ)−C Re 2sin(2πτυ)](20)s o +(−υ)=C 1exp (j 2πτυ)(C Re 3+jC Imag 3)(21)=C 1(C Re 3+jC Imag 3)(cos(2πτυ)+j sin(2πτυ))=C 1(C Re 3+jC Imag 3)cos(2πτυ)+C 1(C Re 3+jC Imag 3)j sin(2πτυ)=[C 1C Re 3cos(2πτυ)+C 1jC Imag 3cos(2πτυ)]+[jC 1C Re 3sin(2πτυ)−C 1C Imag 3sin(2πτυ)]Re s o +(−υ)=C 1(C Re 3cos(2πτυ)−C Imag 3sin(2πτυ))(22)Imag s o +(−υ)=jC 1(C Imag 3cos(2πτυ)+C Re 3sin(2πτυ))(23)s o (υ)=12[s o +(υ)+s o +(−υ)](24)Re s o (υ)=12[s Re o (υ)+s Re o (−υ)(25)Imag s o (υ)=12[s Imag o +(υ)+s Imag o (−υ)](26)where C 2changes for up and down sweep.upsweep (υ)=h (υ−(f o −τB F M /T p ))(27)downsweep (υ)=h (υ−(f o +τB F M /T p ))(28)and C 3changes for upsweep and down sweep upsweep (−υ)=h (−υ−(f o −τB F M /T p ))(29)downsweep (−υ)=h (−υ−(f o +τB F M /T p ))(30)In turn we firstly evaluate equations (27),(28),(29)and (30).This gives us the frequency that we have to search for in the look-up table obtained from the DFT equation (13).The nearest match for the frequency will provide us with an amplitude,Re +Imag ,for C 2.We now have all the terms to evaluate equation (25)and (26).Once we obtain the signal output we find the magnitude.This where we get rid of the imaginary values returned as the amplitude of the DFT is.y (υ)= s o (υ) (31)For now we will ignore noise,just to get the system working.This is repeated for all samples and the results placed in two arrays,one for upsweep and one for down-sweep.When completed the arrays are searched for their peak values.The position of the peak values are noted as these will give us the relevant frequencies of these peak signals.From these we can calculate the range ˆr =cT p 4B F M(ˆf down −ˆf up )(32)4Modelling and Data ExtractionThe software that we use for modelling the objects is SoftImage |3D.This is an extremely powerful piece of software that is mainly used within the film and animation industry.It can produce models of high detail or models can be found on the web which can be purchased or obtained trhrough freewaree.The bought models tend to be of a much higher detail made of many more polygons.This allows for us to offer our simulation software to others for use with a wide variety of ready made models.See figure 3.SoftImage |3D can alsoReferences[1]dar Sensor Modelling and Image Synthesis for ATR Algorithm Development.[2]Jonathan Michael&N.Nandhakumar.Unified3D Models for Multisensor Image Synthesis.[3]Sanjit K.Mitra&James F.Kaiser.Digital Signal Processing.[4]J.V.Black.Fusion of Infrared and Visible-Light Images.[5]N.Nandhakumar&J.K.Agaarwal Integrated Analysis of Thermal and Visual Images for Scene Inter-pretation.[6]J.D.Michel&N.Nandhakumar&Tushar Saxena&Deepak ing Elimination Methods toCompute Thermophysical Algebraic Invariants from Infrared Imagery.[7]W.P.Jones.Air Conditioning Engineering.[8]David DeWitt&Frank P.Incropera.Fundamentals of Heat and Mass Transfer.[9]Press,Flannery,Teukolsky&Vetterling.Numerical Recipes in C.。
SOME APPROACHES AND PARADIGMS FOR VERIFYING AND VALIDATING SIMULATION MODELS
Proceedings of the 2001 Winter Simulation ConferenceB. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, eds.ABSTRACTIn this paper we discuss verification and validation of simulation models. The different approaches to deciding model validity are described, two different paradigms that relate verification and validation to the model development process are presented, the use of graphical data statistical references for operational validity is discussed, and a rec-ommended procedure for model validation is given.1 INTRODUCTIONSimulation models are increasingly being used in problem solving and in decision making. The developers and users of these models, the decision makers using information de-rived from the results of these models, and people affected by decisions based on such models are all rightly concerned with whether a model and its results are “correct”. This concern is addressed through model verification and valida-tion. Model verification is often defined as “ensuring that the computer program of the computerized model and its im-plementation are correct” and is the definition adopted here. Model validation is usually defined to mean “substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model” (Schlesinger et al. 1979) and is the definition used here. A model sometimes becomes accredited through model accreditation. Model ac-creditation determines if a model satisfies specified model accreditation criteria according to a specified process. A re-lated topic is model credibility. Model credibility is con-cerned with developing in (potential) users the confidence they require in order to use a model and in the information derived from that model.A model should be developed for a specific purpose (or application) and its validity determined with respect to that purpose. If the purpose of a model is to answer a variety of questions, the validity of the model needs to be determined with respect to each question. Numerous sets of experimental conditions are usually required to define the domain of a model’s intended applicability. A model may be valid for one set of experimental conditions and invalid in another. A model is considered valid for a set of experimental conditions if its accuracy is within its acceptable range, which is the amount of accuracy required for the model’s intended purpose. This gen-erally requires that the model’s output variables of interest (i.e., the model variables used in answering the questions that the model is being developed to answer) be identified and that their required amount of accuracy be specified. The amount of accuracy required should be specified prior to starting the de-velopment of the model or very early in the model develop-ment process. If the variables of interest are random variables, then properties and functions of the random variables such as means and variances are usually what is of primary interest and are what is used in determining model validity. Several versions of a model are often developed prior to obtaining a satisfactory valid model. The substantiation that a model is valid, i.e., performing model verification and validation, is generally considered to be a process and is usually part of the model development process.It is often too costly and time consuming to determine that a model is absolutely valid over the complete domain of its intended applicability. Instead, tests and evaluations are conducted until sufficient confidence is obtained that a model can be considered valid for its intended application (Sargent 1982, 1984 and Shannon 1975). If a test deter-mines that a model does not have sufficient accuracy for a set of experimental conditions, then the model is invalid. However, determining that a model has sufficient accuracy for numerous experimental conditions does not guarantee that a model is valid everywhere in its applicable domain.The primary purpose of this paper is to discuss the dif-ferent basic approaches to verification and validation, to pre-sent two paradigms for relating verification and validation to the simulation model development process, and to describe the use of graphical data statistical references for compari-son of model and system data in operational validation. SeeSOME APPROACHES AND PARADIGMSFOR VERIFYING AND VALIDATING SIMULATION MODELSRobert G. SargentDepartment of Electrical Engineering and Computer ScienceL.C. Smith College of Engineering and Computer ScienceSyracuse UniversitySyracuse, NY 13244, U.S.A.Sargent (2000) for a general introduction to verification, validation, and accreditation of simulation models.The remainder of this paper is organized as follows: Section 2 presents the basic approaches used in deciding model validity, Section 3 contains two different para-digms used in verification and validation of simulation models, Section 4 discusses operational validation, Sec-tion 5 gives a recommended validation procedure, and Section 6 has the summary.2 BASIC APPROACHESThere are four basic approaches for deciding whether a simulation model is valid or invalid. Each of the ap-proaches requires the model development team to conduct verification and validation as part of the model develop-ment process, which is discussed below. One approach, and a frequently used one, is for the model development team itself to make the decision as to whether a simulation model is valid. A subjective decision is made based on the results of the various tests and evaluations conducted as part of the model development process. However, it is usu-ally better to use one of the next two approaches, depend-ing on which situation applies.If the size of the simulation team developing the model is not large, a better approach than the one above is to have the user(s) of the model heavily involved with the model development team in determining the validity of the simulation model. In this approach the focus of who de-termines the validity of the simulation model should move from the model developers to the model users. Also, this approach aids in model credibility.Another approach, usually called “independent verifica-tion and validation” (IV&V), uses a third (independent) party to decide whether the simulation model is valid. The third party is independent of both the simulation develop-ment team(s) and the model sponsor/user(s). This approach should normally be used when developing large-scale simu-lation models, which usually have one large or several teams involved in developing the simulation model. Also, this ap-proach is often used when a large cost is associated with the problem the simulation model is being developed for and/or to help in model credibility. In this approach the third party needs to have a thorough understanding of what the intended purpose of the simulation model is for. There are two com-mon ways that IV&V is conducted by the third party. One way is to conduct IV&V concurrently with the development of the simulation model. The other way is to conduct IV&V after the simulation model has been developed.In the concurrent way of conducting IV&V, the model development team(s) receives input from the IV&V team regarding verification and validation as the model is being developed. Thus, the development of a simulation model should not progress beyond each stage of development if the model is not satisfying the verification and validation requirements. It is the author’s opinion that this is the bet-ter of the two ways. If the IV&V is conducted after the model has been completely developed, the evaluation per-formed can range from simply evaluating the verification and validation conducted by the model development team to performing a complete verification and validation effort. Wood (1986) describes experiences over this range of evaluation by a third party on energy models. One conclu-sion that Wood makes is that performing a complete IV&V effort is extremely costly and time consuming for what is obtained. This author’s view is that if IV&V is going to be conducted on a completed simulation model then it is usu-ally best to only evaluate the verification and validation that has already been performed.The last approach for determining whether a model is valid is to use a scoring model (see, e.g., Balci (1989), Gass (1993), and Gass and Joel (1987)). Scores (or weights) are determined subjectively when conducting various aspects of the validation process and then combined to determine cate-gory scores and an overall score for the simulation model. A simulation model is considered valid if its overall and cate-gory scores are greater than some passing score(s). This ap-proach is seldom used in practice.This author does not believe in the use of scoring models for determining validity because (1) a model may receive a passing score and yet have a defect that needs to be corrected, (2) the subjectiveness of this approach tends to be hidden and thus this approach appears to be objective, (3) the passing scores must be decided in some (usually) subjective way, and (4) the score(s) may cause over confidence in a model or be used to argue that one model is better than another.3 PARADIGMSIn this section we present and discuss paradigms that relate verification and validation to the model development proc-ess. There are two common ways to view this relationship. One way uses a simple view and the other uses a complex view. Banks et al. (1988) reviewed work using both of these ways and concluded that the simple way more clearly illuminates model verification and validation. We present a paradigm of each way that this author has developed. The paradigm of the simple way is presented first and this paradigm is the author’s preferred one.Consider the simplified version of the model develop-ment process shown in Figure 1. The problem entity is the system (real or proposed), idea, situation, policy, or phe-nomena to be modeled; the conceptual model is the mathe-matical/logical/verbal representation (mimic) of the prob-lem entity developed for a particular study; and the computerized model is the conceptual model implemented on a computer. The conceptual model is developed through an analysis and modeling phase, the computerized model is developed through a computer programming and implemen-tation phase, and inferences about the problem entityFigure 1: Simplified Version of the Modeling Process are obtained by conducting computer experiments on the computerized model in the experimentation phase.We now relate model validation and verification to this simplified version of the modeling process (see Figure 1). Conceptual model validation is defined as determining that the theories and assumptions underlying the concep-tual model are correct and that the model representation of the problem entity is “reasonable” for the intended purpose of the model. Computerized model verification is defined as assuring that the computer programming and implemen-tation of the conceptual model is correct. Operational validation is defined as determining that the model’s out-put behavior has sufficient accuracy for the model’s in-tended purpose over the domain of the model’s intended applicability. Data validity is defined as ensuring that the data necessary for model building, model evaluation and testing, and conducting the model experiments to solve the problem are adequate and correct.In using this paradigm to develop a valid simulation model, several versions of a model are usually developed during the modeling process prior to obtaining a satisfac-tory valid model. During each model iteration, model veri-fication and validation are performed (Sargent 1984). A variety of (validation) techniques are used. (See, e.g., Sar-gent (2000) for a set of validation techniques that are commonly used.) No algorithm or procedure exists to se-lect which techniques to use. Some attributes that affect which techniques to use are discussed in Sargent (1984).A detailed way of relating verification and validation to developing simulation models and system theories is shown in Figure 2. This paradigm was recently developed by this author at the suggestion of Dr. Dale K. Pace of The John Hopkins University Applied Physics Laboratory who be-lieved there was a need for a such a paradigm. (Dr. Pace does considerable work in verification and validation for the U.S. Department of Defense. See Pace (2001a and 2001b) where he uses this paradigm.) This paradigm shows the processes of developing system theories and simulation models and re-lates verification and validation to both of these processes.This paradigm shows a Real World and a Simulation World (See Figure 2.). We first discuss the Real World. There exist some system or problem entity in the real world of which an understanding of is desired. System theories describe the characteristics of the system (or problem entity) and possibil-ity its behavior (including data). System data and results are obtained from conducting experiments (experimenting) on the system. System theories are developed by abstracting what has been observed from the system and by hypothesizing from the system data and results. If a simulation model exists of this system, then hypothesizing of system theories can also be done from simulation data and results. System theories are validated by performing theory validation. Theory validation involves the comparison of system theories against system data and results over the domain the theory is applicable for to determine that there is agreement. This process requires nu-merous experiments to be conducted on the real system.We now discuss the Simulation World, which shows a (slightly) more complicated model development process than the other paradigm. A simulation model should only be de-veloped for a set of well-defined objectives. The conceptual model is the mathematical/logical/verbal representation (mimic) of the system developed for the objectives of a par-ticular study. The simulation model specification is a written detailed description of the software design and specification for programming and implementing the conceptual model on a particular computer system. The simulation model is the conceptual model running on a computer system such that ex-periments can be conducted on the model. The simulation model data and results are the data and results from experi-ments conducted (experimenting) on the simulation model. The conceptual model is developed by modeling the system, where the understanding of the system is contained in the sys-tem theories, for the objectives of the simulation study. The simulation model is obtained by implementing the model on the specified computer system, which includes programming the conceptual model whose specifications are contained in the simulation model specification. Inferences about the sys-tem are obtained by conducting computer experiments (experimenting) on the simulation model. Conceptual model validation is defined as determining that the theories and as-sumptions underlying the conceptual model are consistent with those in the system theories and that the model represen-tation of the system is “reasonable” for the intended purpose of the simulation model. Specification verification is defined as assuring that the software design and the specification for programming and implementing the conceptual model on theFigure 2: Real World and Simulation World Relationships with Verification and Validationspecified computer system is satisfactory. Implementation verification is defined as assuring that the simulation model has been implemented according to the simulation model specification. Operational validation is defined as determin-ing that the model’s output behavior has sufficient accuracy for the model’s intended purpose over the domain of the model’s intended applicability.This paradigm shows processes for both developing valid system theories and valid simulation models. Both are accomplished through iterative processes. To develop valid system theories, which are usually for a specific pur-pose, the system is first observed and then abstraction is preformed from what has been observed to develop pro-posed system theories. These theories are tested for cor-rectness by conducting experiments on the system to obtain data and results to compare against the proposed system theories. New proposed system theories may be hypothe-sized from the data and comparisons made, and also possi-bly from abstraction performed on additional system ob-servation, and these new proposed theories will require new experiments to be conducted on the system to obtain data to evaluate the correctness of these proposed system theories. This process repeats itself until a satisfactory set of validated system theories has been obtained. To develop a valid simulation model, several versions of a model are usually developed prior to obtaining a satisfactory valid simulation model. During every model iteration, model verification and validation are performed. This process is similar to the one for the other paradigm except there is (slightly) more detail given in this paradigm.4 OPERATIONAL VALIDATIONOperational validation is determining whether the simula-tion model’s output behavior has the accuracy required for the model’s intended purpose over the domain of the model’s intended applicability. This is where much of the validation testing and evaluation take place. Since the simulation model is used in operational validation, any de-ficiencies found may be caused by what was developed in any of the steps that are involved in developing the simula-tion model including developing the systems theories or having invalid data. There are numerous validation tech-niques that are used in operational validation. See, e.g., Sargent (2000) for a discussion on these techniques.The major attribute affecting operational validation is whether the system (or problem entity) is observable, where observable means it is possible to collect data on the operational behavior of the system. When a system is ob-servable, it then is possible to compare the output behav-iors of the system and simulation model to determine whether the simulation model has sufficient accuracy. If it is not possible to make these comparisons for several ex-perimental conditions in the simulation model’s domain of applicability, then it is not possible to obtain high confi-dence in the validity of a simulation model.There are three basic approaches used in making these comparisons: (1) using graphs of the system and simulation model data to make a subjective decision, (2) using confi-dence intervals and (3) using formal hypothesis tests. It is preferable to use confidence intervals or hypothesis tests for the comparisons because these allow for objective deci-sions. However, it is frequently not possible in practice to use either of these approaches because (a) the statistical as-sumptions required cannot be satisfied or only with great difficulty (assumptions usually necessary are data inde-pendence and normality) and/or (b) there is insufficient quantity of system data available that causes the statistical results not to be “meaningful” (e.g., the length of a confi-dence interval developed in the comparison of the system and model means is to large for any practical usefulness). As a result, the use of graphs is the most commonly used approach for operational validity and a specific way of do-ing this is presented next. (For a general discussion and references on these three approaches, see Sargent (2000).) We are going to discuss the use of graphical displays of simulation data as statistical references for operational validation. The idea of using graphical displays of data for operational validation was discussed in Sargent (1996). This idea of using simulation data as graphical data statis-tical references was further developed in Sargent (2001). We are going to discuss the use of three graphical displays of data as data statistical references: histograms, box (and whisker) plots, and scatter plots (scattergram or scatter diagram). (See, e.g., Johnson (1994) or Walpole and Myers (1993) for a discussion of histograms, box plots, and scatter plots.) The data for these graphical statistical references come from simulation models. We then com-pare the system data against these graphical data statistical references in performing operational validation.4.1 Histograms and Box PlotsThe data used in histograms and box plots need only to be identically distributed. The data does not need to be inde-pendent or have a specific statistical distribution. The data used may be the observations themselves or some function of subsets of the collected observations (e.g., the sample means from subsets of the collected observations). Usu-ally a large number of data points are needed in histograms used as graphical data statistical references. The more variable the data and the higher the correlation among the data, the more data needed for the histogram. If the refer-ence is a distribution of a variable, then the number of data points should usually be in the thousands, especially if the data have high correlation and extremely variability. If the reference is of sample means, then the number of data points should usually be in the hundreds. Box plots used as graphical data statistical references generally require less data points than histograms.We present two histograms and two box plots devel-oped from observations collected from a simulation model of a single server queueing model having an infinite allow-able queue and a queue discipline of first-come first-served. The distribution of interarrival times is exponential with mean five and the service time is exponentially distri-bution with mean four. Figure 3 is a histogram of the times it took two thousand consecutive customers to go through the queueing model in steady state. These observations are highly correlated and quite variable. We know from queueing theory that the steady state distribution of these times is an exponential distribution with a mean of twenty. One can readily see that the histogram does not give a nice smooth curve. This indicates that more data points should probably be used depending on the accuracy needed for the graphical data statistical reference.In Figure 4 we present a histogram containing one hundred steady state sample means. Each of these data points (sample means) are independent and identically dis-tributed. They were obtained from one hundred separate independent simulation runs (or replications). The observa-tions used for each sample mean were the times it took twenty consecutive customers to go through the queueing model in steady state. Recall that these times are exponen-tially distributed and highly correlated and thus each sam-ple mean contains twenty correlated exponentially distrib-uted observations. One can readily see from this histogram that the resulting sampling distribution is not a t (or nor-mal) distribution. If a more accurate statistical reference is desired, then additional data points (sample means) should be added to the histogram.Figure 3: Histogram of Time in ModelFigure 4: Histogram of Sample MeansFigure 5 contains box plots of the data contained in the histograms contained in Figures 3 and 4. Some software packages that create box plots show outliers as small cir-cles and the software used here does that. One can readily see the skewness of each set of data by looking at the box, whiskers, and outliers in each box plot.Figure 5: Box Plots of Queueing Model Data As an example of the use of histograms and box plots in operational validation, we consider a simulation project by Lowery (1996). A simulation model was developed to predict the mean (average) number of beds used daily (census) in specific hospital units. Operational validation was performed to determine whether the simulation model’s mean census (usage) of beds was within the re-quired accuracy of four beds for large hospital units (i.e., units having a large number of beds). The data entries to be compared were determined. There was a day of week ef-fect. Various histograms and box plots were used to vali-date this model. We will discuss one of the histograms and one of the box plots that were used. Figure 6 contains a histogram of sample means for census on Mondays of one of the hospital units. There were 24 system observations (weeks) available on this unit for Monday census and these observations are correlated. Thus, we use a 24-week aver-age Monday census for our sample means. Observations were generated from the simulation model to obtain fifty independent 24-week average Monday census to be used as the data for a graphical data statistical reference, which is given in Figure 6. (This histogram is a sampling distribu-tion of the 24-week average Monday census. Note that it is not shaped like a t distribution.) One can readily see that the system data point lies within the reference distribution.Figure 6: Histogram of Hospital DataFigure 7 contains box plots of Sunday census observa-tions for the same hospital unit discussed above. The model box plot, which is the graphical data statistical Figure 7: Box Plots of Sunday Census Datareference, is developed from 100 observations (Sunday cen-sus) generated by the simulation model. The system box plot is developed from 24 observations (Sunday census) col-lected on the hospital unit. In comparing the two box plots, it appears that the model has more variability in its Sunday census than the hospital unit. It is this author’s opinion that this pair of box plots has insufficient evidence to determine whether the model’s mean census on Sunday is or is not within the four beds of the hospital’s mean census desired. (Only two of the graphical comparisons used in performing operational validation on this model were presented here. It was concluded that this simulation model was valid based on the numerous comparisons made.)4.2 Behavior GraphsIn operational validation, comparisons should be made be-tween different behavior relationships occurring in the simulation model and those occurring in the system. These comparisons can be made by using behavior graphs (see Sargent (2000)). Behavior graphs use scatter plots to show the relationships between two entities such as parameters, variables, and functions of random variables by plotting paired data on the two entities. The data points plotted can be the observations themselves, which may be relatively few in number, or may be functions of subsets of the ob-servations, which may be based on a large number of ob-servations. There are no specific statistical assumptions required of the data used in behavior graphs. The data can be correlated, have any statistical distribution, and be non-stationary. Behavior graphs used as graphical data statisti-cal references should have a sufficient number of data points in them to enable them to be well defined.We suggest that behavior graphs be developed from simulation model observations to be used as graphical data statistical references. Then behavior graphs be developed from system observations for the same relationships and be compared against the graphical data statistical references to aid in making a subjective decision whether the simulation model has the accuracy needed for validity. It is important that appropriate measures and relationships be selected for the behavior graphs to ensure the simulation model isfor its intended purpose. Some different measures that can be used are means, variances, maximums, and time series of random variables. Relationships that can used are different measures on the same variable, the same measure on two different variables, or different measures on two different variables.To illustrate the use of behavior graphs in model valida-tion, we consider a simulation model of an interactive com-puter system in Anderson and Sargent (1974) where behav-ior graphs were used to validate the simulation model. Three of the behavior graphs that were used are presented inFigure 8: Reaction TimeFigure 9: Response Time versus Queue Length Figures 8, 9, and 10. The relationship between the mean and standard deviation of reaction time is shown in Figure 8. On can readily observe that the same linear relationship occurs in both the simulation model and the computer system. Figure 9 contains the relationship of average response time versus average background queue length. On can readily observe that these model and system relationships are simi-lar except for two system points, which is important to determine why. Figure 10 contains relationships for both the average and maximum observed values of reaction time versus the total number of disk accesses. Each data point is from or represents five minutes of computer system time. We observe that these model and system relationships are similar with the exception that the system has more vari-ability than the model. (For additional behavior graphs and details of the validation of this simulation model, see Anderson (1974) and Anderson and Sargent (1974).)。
simulation modeling practice
Simulation Modeling PracticeSimulation modeling is an essential skill that many professionals use in their work. It involves creating a virtual representation of a system or process, allowing us to test and experiment with different scenarios and outcomes. In this article, we will explore the importance of simulation modeling, how to practice it, and some tips for success.The Benefits of Simulation ModelingSimulation modeling has many benefits, including:* Accuracy: By simulating a system or process, we can eliminate human error and guesswork, ensuring a more accurate representation of reality.* Efficiency: Simulation modeling allows us to test different scenarios and outcomes quickly and efficiently, saving time and resources.* Portability: Simulation models can be easily transferred between different systems or organizations, making them a valuable asset in cross-functional teams.How to Practice Simulation ModelingPracticing simulation modeling requires a combination of knowledge, skills, and resources. Here are some tips for success:* Identify a topic: Start by choosing a system or process that you are interested in simulating. This could be anything from a business process to a mechanical device.* Research resources: Find online resources that can help you learn more about simulation modeling. This could include tutorials, courses, or online communities.* Practice with a partner: Pair up with someone who is also learning simulation modeling and work together on simulating different scenarios. This will help you identify areas where you need more practice and provide feedback on your progress.* Use simulation software: There are many simulation software packages available that allow you to create virtual representations of systems or processes. Practice using these software packages to develop your skills.* Be patient: Learning new skills takes time and practice, so be patient with yourself and your progress.ConclusionSimulation modeling is an essential skill that many professionals use in their work. By practicing simulation modeling, you can improve your skills and become more effective in your job. The benefits of simulation modeling include accuracy, efficiency, and portability. To practice simulation modeling, identify a topic, research resources, practice with a partner, use simulation software, and be patient with yourself and your progress. With these tips in mind, you can become a more effective simulation modeler and achieve success in your career.。
simulation modelling practice
simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。
SimulationWithArena14SolutionsManual
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Cadence Analog Design Environment
Chap 3, Cadence Analog Artist Design Environment, ELEC6970, FDAI, 2004
9
Schematic Entry Flow
Open Design Add Component Instances
Add Pins Add and Name Wires
• Add the following in your .cshrc file: setenv CDS_Netlisting_Mode "Analog "
• Launch Cadence: in “Name6970” by typing icfb& or icms& or msfb
• Cadence Manu: pdf files @ /opt/cadence/ic4.46/doc, use help or type “openbook&” under UNIX.
• CdsSpice, HspiceS, Spectre, spectreS –contain spice information for the element.
• abstract - contains an abstract representation of the layout for use by Cadence place and route software.
Digital and Analog Modulation Using Matlab and Simulink
Teaching Digital and Analog Modulation to Undergradute Information Technology Students Using Matlab and SimulinkM. Boulmalf1, Y. Semmar2 , A. Lakas3, and K. Shuaib31 School of Science & Engineering, Al Akhawayn University in Ifrane, Morocco2 College of Education, Qatar University, Doha, Qatar3College of Information Technology, UAE University, Al Ain, UAEE-mail: m.boulmalf@aui.maAl Akhawayn University in Ifrane , Morocco, P.O. Box: 2129Abstract—Teaching mathematical intensive engineering based courses to undergraduate Information Technology students poses a great challenge to instructors. In this paper we provide an efficient and effective method for teaching digital and analog modulation to undergraduate students enrolled in an Information Technology program which does not require a strong foundation in mathematics as in the case of an Engineering program. The used approach utilizes Matlab packages, Simulink, and Communication Blockset to simulate analog and digital modulation techniques avoiding the derivation of any mathematics formulations and without coding. A survey that was distributed to Information Technology students who were taught using this approach showed a high level of satisfaction in understanding all modulation concepts.Keywords: Matlab; Modulation; Simulink, Communications BloksetI.INTRODUCTIONMatlab is a numerical computing environment and a 4th generation programming language. It is a high level language and interactive environment that enables users to perform intensive calculations based tasks very fast. Developed by Mathworks [5], Matlab allows matrix manipulation, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs in other languages. Matlab has been widely adopted for over 25 years in the academic community, industry and research centers. It was originally written to provide easy access to LINPACK and EISPACK software packages [1-4]. The Matlab software provides the users with a large collection of toolboxes and modules for a variety of applications in many fields of interest.Simulink [6] is an interactive graphical tool that was added to Matlab to make the modeling and simulation of various systems as easy as connecting predefined and designed building blocks. Simulink contains many block sets that are used in almost all applications such as the communication block set and the signal processing block set.Research using Matlab/Simulink has been conducted for many years in academia, industry and also military. Many researchers have published papers using Matlab/Simuling for simulating particular systems. For examples, the authors in [7, 8, 10, 11] used Matlab/Simulink to model components and performance of various wireless communication systems. Others have utilized Matlab/Simulink as a research tool for army and military based applications [13, 14].Matlab has been used as a teaching aid in many subjects such as mathematics, physics, heat conduction, control systems, mechatronics, mechanical design, circuit design, communication theory, random processes, electronics and many more disciplines and applications [4, 5, 9, 12, 15, 16].In this paper, we provide an efficient and effective method for teaching digital and analog modulation techniques to undergraduate students enrolled in an Information Technology program which does not require a strong foundation in mathematics as in the case of an Engineering program. The used approach utilizes Matlab and Similink blocksets to simulate analog and digital modulation techniques. To assess the degree to which Matlab/Simulink helped students to understand the taught concepts, a survey was distributed to students and the results were analyzed using Statistical Package for the Social Sciences (SPSS [25]) and presented in this paper.The rest of the paper is organized as follow: In section 2, analog and digital modulation techniques are introduced. Section 3, discusses the use of Simulink and the communication toolboxes available in Matlab to study modulation techniques. Section 4 shows the results of the students’ survey, and section 5 concludes the paper.II.A NALOG AND D IGITAL MODULATIONIn general, modulation is used to give the transmitted signal properties which are best suited to the transmission channel or environment. Specifically, modulation is the process of imparting the source information onto a band pass signal with a carrier frequency, f c, by the introduction of amplitude or phase perturbations or both. This band pass signal is called the modulated signal and the base band source signal is called the modulating signal [4]. At the receiver end a mean to translate the higher frequencies back to the audio range is implemented and this is called demodulation.A. Amplitude modulation (AM)Audio signals at most occupy the frequency range 0-20 KHz (minimum 15 Km wavelength). This range of frequencies is too low to transmit directly as electromagnetic radiation, particularly due to the prohibitive sizes of the transmitter and receiver antennas which would be required. Antennas must have lengths of the order of the wavelength of the EM radiation of interest. Higher frequencies permit much more effective and practical transmission; however, these lie outside the audio range. For example, AM radio broadcasting occurs at frequencies of the order of 1 MHz.In standard AM, the audio signal is shifted in amplitude by adding a DC component and then multiplied by a sinusoid at the carrier frequency, f c . The carrier frequency is much higher than the audio frequency band. We consider a mathematical description of amplitude modulation. Following the nomenclature of Couch’s textbook [4], let the audio message signal be m(t), where m(t) is band limited to “W” Hz, and let A c be the message amplitude (or gain).Let ωc = 2πf c be the carrier frequency in radians per second where f c >> W. Then the amplitude modulated signal s(t) can be expressed as)cos()](1[)(t t m A t s c c ωμ+= )cos()()cos()(t t m A t A t s c c c c ωμω+=The constant, μ, is selected such that1)(1<<−t m μThe students shall understand very well the aboveformulas and they have to write codes using Matlab to drawthe following curves depicted in Figure 1.Figure 1: AM modulation using Matlab codeFigure 1 depicts the audio signal, the carrier, and the amplitude modulated signal. This result is a coding the above formulas of AM using Matlab codes which is not easy for the non engineering students.B. Frequency modulation (FM)Frequency modulation encodes the message, m(t), by making the instantaneous frequency deviation about f c proportional to m(t ). Frequency Modulation is a special case ofAngle Modulated signaling. In angle modulated signaling the complex envelope is:)()(t j c e A t g θ=Note that this is in polar form so we can immediately say what the amplitude modulation is R (t ) and the phase modulation is simply θ(t). The amplitude modulation is:c A t g t R ==)()(The phase modulation is simply θ(t) and for angle modulated signals is a linear function of the modulating signal m(t). For FM the phase is proportional to the integral of m(t):∫∞−=tf d m D t σσθ)()(Where the frequency deviation constant D f has units of radians per volt-second. So the frequency modulated FM signal is:⎥⎦⎤⎢⎣⎡+=∫∞−tfc cd m D t f A t s σσπ)(2cos )(The bandpass signal is represented by:⎥⎦⎤⎢⎣⎡+=∫∞−tf c c d m D t f A t s σσπ)(2cos )(Figure 2: FM modulationFigure 2 illustrates the Programmed FM modulation using Matlab codes.III. A NALOG AND DIGITAL MODULATION USING SIMULINK AND CO MMUNICATION TOOLBOXESA. SimulinkSimulink, developed by The MathWorks, is a tool for modeling, simulating and analyzing multi-domain dynamic systems. Its primary interface is a graphical block diagramming tool and a customizable set of block libraries. It offers tight integration with the rest of the MATLAB environment and provides scripting capability. Simulink is widely used in control theory and digital signal processing for multi-domain simulation and design.Simulink is integrated with MATLAB, providing immediate access to an extensive range of tools for algorithm development, data visualization, data analysis and access, and numerical computation. The Key Features of Simulink include: [6] •Extensive and expandable libraries of predefined blocks.•Interactive graphical editor for assembling andmanaging intuitive block diagrams.•Ability to manage complex designs by segmenting models into hierarchies of design components. •Model Explorer to navigate, create, configure, and search all signals, parameters, and properties of models. •Ability to interface with other simulation programs and incorporate hand-written code, including MATLABalgorithms.•Option to run fixed- or variable-step simulations of time-varying systems interactively or through batchsimulation.•Functions for interactively defining inputs and viewing outputs to evaluate model behavior.•Graphical debugger to examine simulation results and diagnose unexpected behavior in designs.•Full access to MATLAB for analyzing and visualizing data , developing graphical user interfaces, and creatingmodel data and parameters.•Model analysis and diagnostics tools to ensure model consistency and identify modeling errors.With Simulink, the user can quickly create, model, and maintain a detailed block diagram of a system using a comprehensive set of predefined blocks. Simulink provides tools for hierarchical modeling, data management, and subsystem customization, making it easy to create concise, accurate representations, regardless the system’s complexity. Details on how to create and run simulation models are out of the scope of this paper, for more information on this the reader can look at the following references [6, 17, 18].munication ToolboxCommunications Toolbox extends the MATLAB technical computing environment with functions, plots, and a graphical user interface for exploring, designing, analyzing, and simulating algorithms for the physical layer of communication systems. The toolbox helps create algorithms for commercial and defense wireless or wireline systems, such as mobile handsets and base stations, wired and wireless local area networks, and digital subscriber lines. It can also be used in research and education for communication systems engineering.The Key Features of Communication toolbox includes [5]: •Functions for designing the physical layer of communications links, including source coding, channelcoding, interleaving, modulation, channel models, andequalization.•Graphical plots for visualizing communications signals, such as eye diagrams and constellations. •Graphical user interface for comparing the bit error rate of a system with a wide variety of proven analyticalresults.•Galois field data type for building communications algorithms.•New channel visualization tool to visualize and explore time-varying communications channels. C.Simulation of analog modulationThe students don’t have to derive any more the modulation formulas. He/She has just to understand the components of the equation (e.g. AM equations) and start to implement using the Simulink. Figure 3 illustrates the blocks needed to design an AM modulator. The students have to open simulink and drag and drop in the area of work all these blocks. To visualize the modulated and modulating signals, we need to add to the simulator two scopes. Scope 1 shows the original signal before the modulation. Scope 2 shows the modulated signal, it means after the modulation process. The students can also see the modulated signal in frequency domain. They need to drag and drop the Box “BFFT”. This Box does the Fast Fourier Transform operation and illustrates the signal in Frequency domain. The students are exempted toknow the derivation of FFT formula.Figure 3: The model of AM modulator using Simulink Figure 4 shows the audio signal before modulation; it is depicted by the Scope 1.Figure 4: The modulating signal m(t)Figure 5 illustrates the amplitude modulated signal. Thissignal should be transmitted over the medium.Figure 5: The amplitude modulated signal s(t)The frequency domain spectrum is obtained through a buffered-FFT scope, which comprises of a Fast Fourier Transform of 128 samples which also has a buffering of 64 of them in one frame. Figure 6 depicts the modulated signal infrequency domain.Figure 6: The spectrum of the AM signal s(t)Table 1 below lists and describes the blocks in the Analog Passband sub-library of Modulation by double-clicking on the Analog Passband icon in the main Modulation library, or by typing commanapbnd2 at the MATLAB prompt. For more details, the readers can consult the Mathwork website.TABLE I. T ABLE 1: B LOCKS FOR A NALOG M ODULATIONBlock NamePurpose DSB AM Demodulator Passband Demodulate DSB-AM-modulated dataDSB AM Modulator PassbandModulate using double-sideband amplitude modulationDSBSC AM Demodulator Passband Demodulate DSBSC-AM-modulated data DSBSC AM Modulator PassbandModulate using double-sideband suppressed-carrieramplitude modulation FM Demodulator Passband Demodulate FM-modulateddataFM Modulator Passband Modulate using frequencymodulation PM Demodulator Passband Demodulate PM-modulateddataPM Modulator Passband Modulate using phase modulation SSB AM Demodulator Passband Demodulate SSB-AM-modulated dataSSB AM Modulator PassbandModulate using single-sideband amplitude modulationD. Simulation of Digital modulation Table 2 lists and describes the blocks for digital modulations. Figure 7 illustrates the model for MPSK modulations using Simulink and the Communications block set. The students can vary the parameter “M” and also the Signal to Noise Ratio (SNR) and can easily draw the BER (Bit Error Rate). Figure 8 depicts the results of BER versus the SNR. The student can simulate all type of digital modulations by choosing the adequate Blocks and they can also choose different type of channels. The blocks are respectively Data generator, Modulator, Channel model, demodulator, and the BER calculator. The students are not supposed to know the formula of Probabilities of errors. The model generates a million of bits and can calculate easily the errors.TABLE II. B LOCKS FOR DIGITAL M ODULATIONBlock NamePurposeM-DPSK Demodulator PassbandDemodulate DPSK-modulated data M-DPSK Modulator Passband Modulate using the M-ary differential phase shift keying method M-PSK Demodulator PassbandDemodulate PSK-modulated data M-PSK Modulator Passband Modulate using the M-ary phase shiftkeying method OQPSK Demodulator PassbandDemodulate OQPSK-modulated data OQPSK Modulator Passband Modulate using the offset quadrature phase shift keying method M-FSK Demodulator Passband Modulate using the M-ary frequency shift keying methodM-FSK Modulator PassbandModulate using the M-ary frequency shift keying methodFigure 7: The Block diagram of MPSK ModulationsFigure 8: The BER versus SNR for MPSKFigure 8 shows, for MPSK, as the value of M increase the BER increase. So, MPSK is better in term of BER when the value of M is small.IV. RESULTS AND D ISCUSSIONA 13-item survey was administered to 57 Information Technology undergraduate students. The survey tapped students' reactions to using Matlab with Simulink for learning and understanding digital and analog modulation concepts. The convenience sample of participants responded to the survey, which was based on the following, 5-point Likert scale: 5 = Strongly Agree; 4 = Agree; 3 = Somewhat Agree; 2 = Disagree; 1 = Strongly Disagree. As can be seen from Table 3, students expressed a general consensus towards their agreement of the benefits of incorporating Matlab with Simulink in their course. Most of them, for instance, claimed that the Matlab with Simulink component was very useful for helping them understand theTABLE III.Descriptive S TATISTICS (N=57)Variable Min. Max. Mean SDKnowledge before 11 5 3.02 1.009 Knowledge after 22 5 4.14 .766 Attitude3 2 5 3.71 .762 Understanding4 25 4.04 .731 Learnmore 5153.69.979Easy to use 6 11 5 3.68 1.003 Approving 7 1 5 3.50 .885 Simulation experience 8 1 5 3.55 .807 Learning experience 91 5 3.68 .917 Interact 102 5 3.81 .693 Educational 11 2 5 3.89 .673 Comfortable 12 2 5 3.86 .718 Satisfied 13253.74.813theoretical aspect of the course, thereby increasing their knowledge base of the subject matter.Independent Samples T-Tests were run to investigate differences between the 2006 and 2007 cohorts in terms of how they perceive the usefulness of Matlab with Sumilink. As can be seen in Table 4, higher means were observed for the 2007 students. This group of students held more favorable views about the utility of Matlab with Sumilink in their course. For example, compared to their 2006 counterparts, the 2007 cohort felt more strongly that Matlab with Simulink was easy to use (mean = 4.41), helped them understand the course much better (mean = 4.45), and that their knowledge has now increased as a result of using Matlab with Sumilink (mean = 4.55).TABLE IV. T-Test (N=57)Variable N Year Mean SD Knowledgebefore 1 35 22 2006 2007 2.89 3.23 1.051 .922 Knowledgeafter 235 22 2006 2007 3.89 4.55 .128 .127 Attitude 3 35 20 2006 2007 3.57 3.95 .118 .185 Understanding 4 35 22 2006 2007 3.77 4.45 .117 .127 Learnmore 5 34 21 2006 2007 3.32 4.29 .945 .717 Easy to use 6 35 22 2006 2007 3.23 4.41 .154 .142 Approving 733 21 2006 2007 3.27 3.86 .164 .143 Simulation experience 834 222006 2007 3.32 3.91 .138 .146 Learning experience 935 212006 20073.404.14.154 .159Interact 10 3522 200620073.574.18.111.125Educational 11 3522 200620073.634.32.101.121Comfortable 12 3522 200620073.634.23.117.130Satisfied 13 3522 200620073.434.23.131.130A. Internal consistency reliabilityA Pearson correlation matrix of the 13 Matlab with Simulink questionnaire items was run yielding a Cronbach's alpha coefficient of .91 (α = .91). The 13-item questionnaire was then subjected to factor analysis using principal axis factoring to extract the underlying factors.B.Factor AnalysisData from the 13-item, Matlab with Simulink questionnaire was analyzed using principal axis factoring (SPSS 14.0) to extract the underlying factors. Principal axis factoring is preferred over the principal components analysis method, which is the default option in some statistical programs including SPSS. Since it is assumed that in principal components analysis all variability in an item ought to be used, it is advantageous to use the principal axis factoring method through which the researcher can only use the variability in an item that it shares with the other items [20]. The number of factors to be extracted was based on minimum eigenvalues of 1.0 and minimum loadings of .45 of individual items under each factor. The Kaiser-Gutman procedure, through which only factors with eigenvalues of one or greater are selected, is the most often used method to determine the number of factors [21]. The varimax rotation method produced a two-factor solution, accounting for almost 62% of the total variance (see Table 7). Varimax rotation was employed because it is a type of orthogonal rotation that mathematically ensures that the resulting factors are uncorrelated with each other [21]. This is important since in exploratory factor analysis, the researcher does not know the number and the types of factors that exist, let alone whether or not they are correlated [19].1)Factor 1: Factor 1 consisted of eleven items from the Matlab with Simulink questionnaire. The following eleven items loaded on factor 1(Table 5): TABLE V. Eleven items from the Matlab & Simulink Questionnaire2)Factor 2: Factor 2 consisted of two items from the Matlab with Simulink questionnaire. The following two items loaded on factor 2 (Table 6):TABLE VI. Two items from the Matlab & Simulink Questionnaire In sum, results of the factor analysis portion of this study suggested two components that characterize the School of IT students' perceptions and attitudes towards the benefits of incorporating Matlab with Simulink into the course curriculum. Factor 1 can be labeled "Motivation" since all of the items that loaded onto it are likely to contribute to learners' engagement and motivation in the classroom because of the Matlab with Simulink component. The two items that loaded on factor 2 are strictly related to students' knowledge level before and after their experience with the Matlab Simulink. Therefore, Factor 2 can be labeled as "Knowledge Competence" (See Table 8).TABLE VII. Principal Axis Factoring (13 questionnaire items)Factor Eigenvalue % of Variance Cumulative %1 6.749 51.915 51.9152 1.305 10.038 61.953TABLE VIII. Factor StructureFactor Label Eigen-value VarianceCumulativeVariance1 Motivation 6.749 51.915 51.9152 KnowledgeCompetence1.305 10.038 61.953V.CONCLUSIONIn this paper, the approach utilizes Matlab packages, Simulink, and Communication Blockset to simulate analog and digital modulation techniques avoiding the derivation of any mathematics formulations. A survey that was distributedto 57 Information Technology students who were taught using this approach showed a high level of satisfaction in understanding all modulation concepts. As can be seen from the survey results, students expressed a general consensus towards their agreement of the benefits of incorporating Matlab with Simulink in their course. Most of them, for instance, claimed that the Matlab with Simulink component was very useful for helping them understand the theoretical aspect of the course, thereby increasing their knowledge basedon the subject matters.REFERENCES[1]Gilat, Amos (2004). MATLAB: An Introduction withApplications 2nd Edition. John Wiley & Sons. ISBN 978-0-471-69420-5.[2]Quarteroni, Alfio; Fausto Saleri (2006). ScientificComputing with MATLAB and Octave. Springer. ISBN978-3-540-32612-0.[3]Ferreira, A.J.M. (2009). MATLAB Codes for FiniteElement Analysis. Springer. ISBN 978-1-4020-9199-5. [4]Leon W. Couch, II. Digital and Analog CommunicationSystems. Prentice Hall, New Jersey, sixth edition, 2001. [5]/access/helpdesk_r13/help/toolbox/commblks/ref/simref-7.html#611864[6]/products/simulink[7]Etter, D.M. ``Engineering Problem Solving usingMATLAB'' Prentice Hall, 1993.[8]The International Journal of Engineering Education, Vol.21, number 5, 2005.[9]John Okyere Attia, “Teaching AC Circuit Analysis withMatlab”, the World Wide Web electronic version of the1995 ASEE/IEEE Frontiers in Education ConferenceProceedings.[10]M. Alnuaimi, K. Shuaib and I. Jawhar, “PerformanceEvaluation of IEEE 802.15.4 Physical Layer UsingMatlab/Simulink”, Proceeding of the IEEE IIT06 Conference, Nov19-21, 2006, Dubai, UAE.[11]M. Boulmalf, A. Sobh, A. Shakil "Modeling andsimulation of 802.11g WLAN using Matlab and Simulink," Advanced Simulation TechnologiesConference, April 18 - 22, 2004, Crystal City Arlington,Virginia.[12]Attia, J.O, “Teaching electronics with MATLAB”,Proceeding of Frontiers in Education Conference, 1996,Salt Lake City, USA.[13]Anderson, C. Kitts, C, “A MATLAB expert system forground-based satellite operations”, Proceeding of theAerospace, 2005 IEEE Conference, 5-12 March 2005,page(s): 3756 - 3762[14]Agustina, J.V.; Peng Zhang; Kantola, R.; “Performanceevaluation of GSM handover traffic in a GPRS/GSMnetwork”, Proceeding of ISCC, 2003 Page(s):137 - 142vol.1[15]Bhatt, T.M.; McCain, D, “Matlab as a developmentenvironment for FPGA design”, Proceeding of DesignAutomation Conference, 2005. 13-17 June 2005Page(s):607 – 610[16]“Development of a MATLAB-Based Model forAdvanced High Power Density Diesel Engine for MilitaryApplications” Project /arc/research/ta4/T4DevMatlabModelAdvHPD.htm[17]Mehrdad Soumekh – “Synthetic Aperture Radar SignalProcessing With Matlab Algorithms”, John Wiley & SonsInc; Apr 1, 1999.[18]B. L. Sturm and J. Gibson, "Signals and Systems UsingMATLAB: An Integrated Suite of Applications forExploring and Teaching Media Signal Processing," inProc. of the 2005 IEEE Frontiers in Education Conf.(FIE), Indianapolis, IN (2005).[19]Bachiller, C. Esteban, H. Cogollos, S. San Blas, A.and Boria, V.E. “Teaching of wave propagationphenomena using MATLAB GUIs at the UniversidadPolitecnica of Valencia”, the IEEE Antennas andPropagation Magazine, Feb. 2003 Volume: 45, page(s):140 – 143.[20]/~dghosh/homework/Simulink_Tutorial.pdf[21]/academia/student_center/tutorials/index.html? link=body[22]D. Child. The essentials of factor analysis. London:Cassell, 1990.[23]R.L. Gorsuch. Common factor anaylsis versus componentanalysis: Some well and little known facts. MutivariateBehavioral Research, 25, 33-39, 1990.[24]J.C. Loehlin. Latent variable models: An introduction tofactor, path, and structural analysis (third edition).Manwah, NJ: Lawrence Erlbaum Associates, 1998.[25] /。
蒙特卡洛分析
3. Go to toolMonte Carlo in affirma analog artist
Monte Carlo simulation (Analyzing waveform)
Matching(VSWR):It tells how well input and output N/W are matched.
VSWR1
VSWR2
Variations in VSWR
Normal simulation Monte Carlo simulation
Bias N/W
Input matching
Linearity
Output matching
Cascode arch.to reduce feedback capacitance
Monte Carlo simulation
1. Choosing affirma analog artist
2. Choosing Spectre simulator
Normal simulation Monte Carlo simulation
Monte Carlo simulation (Analyzing waveform)
Stability:A Kf value >1,is desired for an stable amplifier Kf value has become <1,and consequently creating a potential unstability,hence a large margin is required at initial design phase.
Cadence和SpectreRF教程
Cadence和SpectreRF教程⿇省理⼯学院电⽓⼯程与计算机科学系6.776⾼速通信电路2005年春Cadence和SpectreRF教程Albert Jerng02/13/05引⾔本教程将介绍使⽤Cadence和SpectreRF在6.776 课程⾥对电路进⾏仿真。
Cadence包含了IC设计的整个设计流程的所有⼯具,包括电路原理图、版图、电路仿真和验证⼯具。
我们将在⿇省理⼯学院的SUN服务器上运⾏Cadence 4.4.6版本。
Spectre 电路仿真器需要在Cadence的设计框架中的Affirma模拟设计环境下运⾏。
Spectre是⼀种先进的SPICE仿真器,它可以在差分⽅程级进⾏模拟和数字电路的仿真。
SpectreRF 还包括⼀些附加的仿真功能,如周期稳态(PSS)分析,S参数分析及⾮线性噪声分析,这些分析将使射频电路的仿真更加容易。
本教程将⾸先介绍如何在美国⿇省理⼯学院服务器上获得6.776 课程的Cadence运⾏环境。
然后,给出两个例⼦帮助你熟悉SpectreRF电路仿真器。
运⾏Cadence1 登录到⿇省理⼯学院的SUN服务器2 键⼊以下命令⾏:add 6.776source /mit/6.776/setup_cadence你可以添加这些命令⾏到你的.cshrc.mine⽂件,这样你就不必每次重复这⼀步。
如果发⽣改变,你必须键⼊source.cshrc.mine。
3 ⾸次运⾏Cadence时,remove或move你的?/cds⽬录,然后键⼊:CadenceCadence 446 就可以启动了,并且会创建⼀个包含6.776所需⽂件的⽬录?/cds。
这时,你应该会看到icfb和Library Manager这两个视窗,在Library Manager,你会看到以下的之前下载的⽂件夹:6776_Examples , 6776_Primitives , analogLib ,basic6776_Primitives包含我们这节课将会⽤到的NMOS和PMOS晶体管symbols。
Symbolic simulation---techniques and applications
Symbolic Simulation—Techniques and ApplicationsRandal E.BryantSchool of Computer ScienceCarnegie Mellon UniversityPittsburgh,PA15213AbstractSymbolic simulation involves evaluating circuit behaviorusing special symbolic values to encode a range of circuitoperating conditions.In one simulation run,a symbolicsimulator can compute what would require many runs ofa traditional simulator.Symbolic simulation has appli-cations in both logic and timing verification,as well assequential test generation.The concept of symbolic simulation has been discussedfor over10years,but early attempts had only limitedsuccess.The recent introduction of more powerful,al-gorithmic methods of symbolic manipulation have had amajor impact on the classes of circuits and properties thatcan be evaluated symbolically.1IntroductionA single run of a conventional simulator provides limited information about the behavior of a digital system.It de-termines only how the circuit would behave for a single initial state,input sequence,and set of circuit parameters. Characterizing the system for all possible operating con-ditions by this means is at best impractical and at worst impossible.Many CAD tasks require more extensive information than can be obtained by a single simulation run.For ex-ample,the formal verification of a design requires show-ing that the circuit will behave properly for all possible initial states and input sequences.Automatic test gener-ation requires selecting a subset from among the set of all input sequences that will detect a given set of faults. Clearly,conventional simulation is of little use for such tasks.Most CAD researchers have solved these problems by means other than simulation.For example,the most common approach to formal verification is by theorem.This symbolic extension is small—it covers only two different circuit operating conditions—but the basic idea is there. As a second example,concurrent fault simulation[15] can be viewed as a form of symbolic simulation.This symbolic extension allows one to characterize the behav-ior of the circuit under fault-free and many different faulty conditions simultaneously.The fault list data structures can be viewed as representing elements of a symbolic do-main encoding the value of a signal in the good and all faulty machines.The list manipulations and gate evalua-tions performed while processing the input lists for a logic gate can be viewed as implementing an extension of the gate function to this symbolic domain.Although this is an unconventional view of concurrent fault simulation,it serves to demonstrate the value of data abstraction.That is,we can distinguish between the abstract domain over which the simulation is performed versus the detailed data structures used to represent elements of this domain. The above two examples illustrate how well-known CAD algorithms can be viewed as making use of sym-bolic evaluation.In this paper,we will describe the basic principles of symbolic simulation and some novel ways it can be used.2Historical PerspectiveAs already illustrated,the basic principles of symbolic simulation have implicitly been used by CAD programs for many years.The term was introduced explicitly in the late1970’s by researchers at IBM interested in reason-ing about circuits represented at the register-transfer level [6,9].Their approach drew on techniques developed for reasoning about software by symbolic execution.That is, to evaluate the effect of a sequence of circuit operations, they introduced symbolic values to represent both the pos-sible initial contents of the circuit registers as well as the possible data values applied to the inputs.They extended the primitives of the simulator to operate over expressions involving these symbolic values.Other researchers continued this work into the early 1980’s[8],but activity died off within a few years.People discovered that this form of symbolic evaluation was not powerful enough for reasoning about overall circuit behav-ior.For example,if the simulator reached a conditional branch while evaluating a sequence of control operations, it would simply evaluate both paths of the branch,but la-bel the outcomes with the conditions under which each branch would be taken.After simulating a number of such branches,the expressions would become too large and cumbersome to use effectively.Looping constructs proved even more intractable,since in general the simu-lator could not determine the conditions under which the loop would terminate.The success of this early work was limited by the weak-ness of the symbolic manipulation methods.During the course of a symbolic simulation,these programs built up algebraic expressions that closely reflected the evaluations that had been performed.Consider,for example how such a program would simulate the effect of adding the con-tents of two registers and then adding this to the contents of a third register.It wouldfirst introduce symbolic values ,,and,to indicate the register values.After simu-lating the two operations,the program would produce an expression1.Now suppose that anothersequence of operations summed these numbers in a differ-ent order to produce an expression2, and that a conditional branch were taken based on a com-parison between1and2.For such a simple example, a simple algebraic manipulator could exploit the laws of commutativity and associativity to show that the two ex-pressions are always equal.For more complex cases,how-ever,it becomes very difficult to detect such properties in the symbolic expressions.3Recent ActivityIn the past few years,there has been a renewed interest insymbolic simulation.These recent efforts have been char-acterized by a more algebraic approach to the problem.That is,rather than build up symbolic expressions based on the evaluation sequence,these programs use represen-tations that are based more strongly on the underlyingsymbolic domain.The signal values are represented inways that facilitate the evaluation of the simulation func-tions over the extended domain.This algebraic approach allows the symbolic simulator to reason more effectivelyabout the actual circuit function.Several different algebras have been used depending onthe class of problems the symbolic simulator is intendedto solve.3.1Boolean Algebraic ApproachesBoolean algebra is the most natural domain for reasoningabout digital circuits,since it directly reflects the binarynature of digital signals.Expressing simulation opera-tions in Boolean algebra also leads directly to a symbolicformulation.That is,if we introduce a set of Boolean variables,then we can let our symbolic domain be the setof Boolean functions over these variables.We then rede-fine the logic operations AND,OR,and NOT to operate overfunctions rather than over the values0and1.This givesus an algebra that satisfies all of the laws of a Boolean al-gebra.Thus,any simulation step that can be expressed interms of Boolean operations can,in principle,be evaluatedsymbolically.As an example,our own work expresses all aspectsof a switch-level simulation algorithm in terms of Booleanoperations[4].We use Ordered Binary Decision Diagrams(OBDDs)[3]to represent the Boolean functions created and manipulated by the simulator.This representation hasthe advantage that it is canonical,i.e.,each function has aunique representation.Furthermore,the representation isreasonably compact for many of the functions encounteredin evaluating digital circuit functions.Let us return to the example of the register transfer op-erations described above.Rather than representing the contents of a register with a single integer-valued symbol ,we would represent it as a vector of Boolean-valued symbols10,where is the width of the regis-ter.In doing this,we exploit the fact that actual hardwareworks withfinite precision numbers,and hence we do not need to reason about arbitrary integers.This is fortunate, since many properties of the integers are undecidable.As simulation progresses,the circuit signals are expressed in terms of Boolean functions over these symbols.Thus,in summing the contents of the3registers,we would de-rive OBDDs(one for each bit of the sum)over the3variables:10,10,and10. While this might seem more cumbersome than construct-ing the expression,it has the advantage that we would derive the exact same OBDDs regardless of the way these values are summed.Thus,our symbolic ma-nipulator is able to detect all possible relations among the signal values in the circuit.A Boolean algebraic approach solves many of the prob-lems encountered during early attempts at symbolic simu-lation.For example,conditional branches cause no prob-lems.We simply follow both branches and combine the two results with a“multiplexor”function.Even the func-tion resulting from a looping construct can be constructed by simply simulating the evaluation of the loop repeatedly until it“terminates,”i.e.,the conditional expression en-abling loop execution evaluates to the constant function0. In essence,the simulator evaluates the loop for the worst case number of times.It is obvious that Boolean algebra can be used for rea-soning about two-valued digital models.Many digital circuit models,however,are based on multi-valued signal domains.Rather than developing new symbolic repre-sentations for these signal domains,we can often encode the elements with multiple Boolean values.Our register transfer example above,for instance,illustrated an encod-ing of afinite precision integer by a set of Boolean values according to a binary representations.Similarly,our own simulator encodes three-valued signals by pairs of Boolean values.That is,each signal01,is represented by a pair of Boolean signals and,according to the following encoding:101extended to make a more detailed analysis of the effects of unknown signal values[5].That is,for each uninitialized state variable in the simulation,they assign an initial value.Rather than treating each of these initial values as a distinct Boolean variable,however,they view them as unknown,but annotated values.During the course of simulation,they represent each signal as either0,1,,1.Whenever unknown signals originating from different sources interact,however,an signal results.This work indicates how adding a limited amount of symbolic reasoning to a simulator can improve its accuracy in dealing with unknown values without incurring the high overhead of full-fledged symbolic simulation.4Applications and ResultsThe original work on symbolic simulation was directed toward formal circuit verification.This remains the major application for symbolic simulation today.The purpose of formal verification is to prove that the circuit will behave properly for all possible operating conditions.For com-binational circuits,verification by symbolic simulation is (conceptually)straightforward.The verifier introduces Boolean variables for each primary input and simulates the circuit operation to compute a Boolean function for each primary output.It then compare these functions with ones derived from the circuit specification.For sequential circuits,the methodology by which one applies a symbolic simulator to prove circuit correctness is more complex.For cases where the specification and the circuit are sequential circuits using the same state encod-ing,we can simply verify the combinational logic portion of the circuit.For cases where the specification is given in some other form,or where the state encodings differ, more sophisticated approaches are required.In one recent approach[2],both the circuit and the specification are represented as sequential systems,but with different state encodings.The user must explicitly specify the relation between the two state encodings in terms of an abstrac-tion function,mapping a circuit state into a corresponding specification state.A second application for symbolic simulation has been to automatic test generation.This approach to the test generation problem differs markedly from the more tra-ditional,search-based approaches.In our work[7],we view test generation as proceeding by symbolic fault sim-ulation.That is,we simulate the good and faulty circuits over a set of symbolic patterns,effectively evaluating their behaviors over many different actual patterns.We then use Boolean manipulation to derive a set of input sequences that will cause the good and faulty circuits to produce dif-ferent outputs.This approach has several advantages over search-based methods.Most significantly,it extends quite naturally to both sequential and switch-level circuits.We have demonstrated the ability of our program to generate tests for a number of moderate-sized sequential circuits, but much further work is needed to make this approach truly practical.The memory required to store the OBDDs representing the good and faulty circuit behaviors limits the scale of circuits that we can handle.5ConclusionsSymbolic simulation has already produced practical results in formal circuit verification and automatic test genera-tion.The recent advent of algebraic methods employing powerful symbolic manipulation techniques has greatly enhanced the capabilities of these programs.The future prospects appear quite promising,as researchers try new symbolic domains,new manipulation algorithms,and new applications.References[1]H.G.Barrow.Proving the Correctness of DigitalHardware Designs.VLSI Design V ol.V,No.7(July 1984),64–77.[2]S.Bose and A.L.Fisher,“V erifying Pipelined Hard-ware Using Symbolic Logic Simulation,”Interna-tional Conference on Computer Design,October, 1989.[3]R.E.Bryant.“Graph-Based Algorithms for BooleanFunction Manipulation,”IEEE Transactions on Computers,V ol.C-35,No.8(Aug.,1986),pp.677–691.[4]R.E.Bryant,et al,“COSMOS:A Compiled Sim-ulator for MOS Circuits,”24th Design Automation Conference,1987,pp.9–16.[5]J.L.Carter,et al,“Restricted Symbolic Evaluationis Fast and Useful,”ICCAD-89,pp.38–41.[6]W.C.Carter,W.H.Joyner,Jr.,and D.Brand,“Sym-bolic Simulation for Correct Machine Design,”16th ACM/IEEE Design Automation Conference,1979, pp.280–286.[7]K.Cho,and R.E.Bryant,“Test Pattern Generationfor Sequential MOS Circuits by Symbolic Fault Sim-ulation,”26th ACM/IEEE Design Automation Con-ference,June,1989,pp.418–423.[8]W.E.Cory,“Symbolic Simulation for FunctionalV erification with ADLIB and SDL,”18th ACM/IEEE Design Automation Conference,1981,pp.82–89. [9]J.A.Darringer,“The Application of Program V eri-fication Techniques to Hardware V erification,”16th ACM/IEEE Design Automation Conference,1979, pp.375–381.[10]G.G.E.Gielen,H.C.C.Walscharts,and W.M.C.Sansen,“ISAAC:A Symbolic Simulator for Analog Circuits,”Journal of Solid-State Circuits,V ol.24, No.6(December,1989),pp.1587–1597.[11]N.Ishiura,M.Takahashi,and S.Yajima,“Time-Symbolic Simulation for Accurate Timing V erifi-cation of Ansynchronous Behavior of Logic Cir-cuits,”26th ACM/IEEE Design Automation Confer-ence,1989,pp.497–502.[12]N.Ishiura,Y.Deguchi,and S.Yajima,“Coded Time-Symbolic Simulation Using Shared Binary Decision Diagram,”27th ACM/IEEE Design Automation Con-ference,1990.[13]J.P.Roth,Computer Logic,Testing,and V erification,Computer Science Press,1980.[14]C.-J.Seger,and R.E.Bryant,“Modeling of CircuitDelays in Symbolic Simulation,”IFIP Workshop on Applied Formal Methods for VLSI Design,Novem-ber,1989.[15]E.Ulrich,and T.Baker,“The Concurrent Simulationof Nearly Identical Digital Networks”,IEEE Com-puter(April,1974),pp.39–44.。
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Other Simulators
• In addition to Cadence SPICE and Spectre circuit simulator popular analog and microwave simulators can be used through a set of integrated simulation interfaces.
More About Direct Simulation
• Important Benefits of Direct Simulation
– Improved performance in netlisting. – Improved performance of simulation for Spectre. – Readable netlists. – Read only design can be simulated provided that they are extracted. – Improved support of standalone netlisting.
Spice, PSpice and HSpice
• SPICE was originally developed at the Electronics Research Laboratory of the University of California, Berkeley (1975) • PSpice is a PC version of SPICE (MicroSim Corp.) • HSpice is a version (Avant!.) that runs on UNIX workstations and larger computers. This is particularly fast version and one that should be nimulators Eg. Spectre
Socket Simulators Eg. SpectreS, cdsSpice
Direct Simulation Vs Socket Simulation
• Direct Simulation
– This is a preferred method because uses the new features added to Spectre simulator and uses direct simulation. – With direct simulation, the netlist uses the syntax of the simulator w/o any processing to evaluate expressions. – The netlist is a direct reflection of the design.
• RF Capabilities • Mixed Signal Simulation
• Spectre circuit simulator coupled with the Verilog-XL simulator in the AFFIRMA environment can simulate mixed analog and digital circuit
• HSPICE achieves upto 20x speed up for cell characterization applications where speed, power and noise are most important.
• HSPICE RF For High Frequency & RF Designs:
– Eg. Meta Software’s HSPICE circuit simulator, HP’s MNS microwave simulator, Compact Software’s Harmonica microwave simulator.
• We have AFFIRMA HSPICE interface installed on our system. • We do not have a HSPICE simulator installed yet.
• Socket Simulation
– The netlist is processed by Cadence SPICE to evaluate all expressions and resolve passed parameters. – Socket methodology is used to integrate a simulator if the current simulator cannot handle expressions or parameters passing. – Efficient operation in various interactive mode such as simulation stop and restart or change values and resimulate.
Cadence SPICE
• Cadence SPICE simulator is an interactive circuit simulator based on UC Berkley’s SPICE2 program. • Modified architecture for interactive operations plus enhancement that automatically improve convergence with problem circuit. • Can be used within the Analog simulation environment or as a standalone simulator.
Cadence DFII Architecture
HSPICE Simulator
• Gold standard for accurate circuit simulation. • Extensive set of build in devices, models including models for small geometry MOSFET and MESFET. • Compatible with Spice and MSING input format. • Cadence supports a library of primitives and a full interface of HSpice. • High Performance:
• Environment
• Fully integrated into Cadence DFII for AFFIRMA and also in Cadence Analog workbench design system
SPICE compatibility of Spectre
• SPICE is a industry standard language with many variations of SPICE syntax on market today. • Each vendor modifies it with different capabilities and/or slightly different syntax. • For convince of SPICE users AFFIRMA Spectre simulator provides SPICE Reader as an extension to its native language that accepts most variations of SPICE input. • SPICE Reader supports SPICE2, SPICE3, and common extensions found in other simulators like PSPICE and HSPICE.
• Signal Integrity:
• HSPICE simulates enhanced W – elements and extracted S – parameters for accurate signal integrity analysis of PCB’s.
Inverter Example
Improvements of Spectre over SPICE
• Improved Capacity
• Can simulate larger circuit.
• Improved Accuracy
• Improved component models and core simulator algorithms
Simulators in the Affirma Analog Design Environment
Sachin Shinde Xiaolai He
• Cadence design framework II environment consists of many Cadence tools which are interoperable without requiring data conversion. • DFII is an open system allowing the user to integrate third party tools like simulators using programmable netlister or enter their own design data.