Preface |
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xv | |
Acknowledgments |
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xix | |
PART 1: INTRODUCTION |
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1 | (46) |
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1 How the Workplace Supports Successful Design |
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3 | (12) |
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1.1 High-Speed Digital Design Is Challenging |
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3 | (3) |
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1.2 Needs for Technical Specialization |
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6 | (1) |
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1.3 The Role of Processes and Procedures |
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7 | (1) |
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1.4 Using Judgment When Making Design Tradeoffs |
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8 | (1) |
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1.5 HSDD Needs the Help of EDA Tools |
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9 | (1) |
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1.6 HSDD Needs a Team That Extends Beyond the Company |
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9 | (1) |
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1.7 HSDD Team Members Often Have Their Own Agendas |
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10 | (1) |
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1.8 HSDD Simulations Performed in the Workplace |
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11 | (1) |
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1.9 Modeling and Simulation Versus Prototype and Debug |
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12 | (1) |
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1.10 Ten Tips for Modeling and Simulation |
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13 | (1) |
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13 | (2) |
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2 Introduction to Modeling Concepts |
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15 | (32) |
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2.1 Modeling and Simulation for All Scales of System Size |
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15 | (1) |
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2.2 Communicating Across Specialties |
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15 | (1) |
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16 | (2) |
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18 | (2) |
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2.5 Needs for Model Accuracy Change as a Design Progresses |
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20 | (2) |
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2.6 There Are Many Kinds of Models and Simulations |
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22 | (1) |
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2.7 Modeling and Simulation for Systems |
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23 | (1) |
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2.8 Bottom-Up and Top-Down Design |
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24 | (3) |
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2.9 Analog Issues in Digital Design |
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27 | (7) |
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2.10 Noise Modeling on Electrical Signals |
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34 | (2) |
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2.11 Additional Design Issues to Model and Simulate |
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36 | (5) |
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2.12 Using EDA Tools for Semiconductors |
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41 | (2) |
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2.13 Using EDA Tools for Board Interconnections |
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43 | (2) |
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2.14 Looking Ahead in the Book |
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45 | (1) |
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45 | (2) |
PART 2: GENERATING MODELS |
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47 | (104) |
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3 Model Properties Derived from Device Physics Theory |
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49 | (46) |
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49 | (1) |
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3.2 Why Deep Sub-Micron Technology Is Complex |
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50 | (2) |
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3.3 Models Extracted from Semiconductor Design Theory |
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52 | (1) |
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3.4 Example of the BJT Process |
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53 | (1) |
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3.5 How BJT and FET Construction Affect Their Operation |
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54 | (11) |
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3.6 Calculating Device Physics Properties |
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65 | (6) |
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3.7 Examples of Computing Electrical Properties from Structure |
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71 | (4) |
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3.8 Examples of SPICE Models and Parameters |
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75 | (15) |
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3.9 Modeling Packaging Interconnections |
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90 | (3) |
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93 | (2) |
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4 Measuring Model Properties in the Laboratory |
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95 | (38) |
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4.1 Introduction to Model Measurements |
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95 | (2) |
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97 | (6) |
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4.3 Scattering-Parameter Models |
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103 | (3) |
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106 | (8) |
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114 | (12) |
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4.6 Web Sites for IBIS Visual Editors and Other Tools |
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126 | (1) |
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4.7 TDR/TDT - VNA Measurements |
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126 | (1) |
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127 | (3) |
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4.9 Field Solver RLGC Extraction for ICs |
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130 | (1) |
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4.10 What is Model Synthesis? |
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130 | (1) |
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4.11 Test Equipment Providers |
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130 | (1) |
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4.12 Software for Test Equipment Control |
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131 | (1) |
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132 | (1) |
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5 Using Statistical Data to Characterize Component Populations |
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133 | (18) |
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5.1 Why Process Variation Is Important |
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133 | (1) |
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5.2 Achieving Process Control with Population Statistics |
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133 | (1) |
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5.3 Basics of Population Statistics |
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134 | (10) |
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5.4 Characterization for Six-Sigma Quality |
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144 | (5) |
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5.5 Six-Sigma Quality for Modeling and Design |
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149 | (1) |
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150 | (1) |
PART 3: SELECTING COMPONENTS AND THEIR MODELS |
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151 | (108) |
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6 Using Selection Guides to Compare and Contrast Components |
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153 | (16) |
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6.1 Tools for Making Component Choices |
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153 | (2) |
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6.2 Team Members Use of Selection Guides |
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155 | (1) |
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6.3 Selection Guide Examples |
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156 | (5) |
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6.4 Selection Guides Help Component Standardization |
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161 | (1) |
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6.5 Simulation as a Selection Guide |
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161 | (5) |
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166 | (1) |
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167 | (2) |
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7 Using Data Sheets to Compare and Contrast Components |
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169 | (30) |
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7.1 Data Sheets as Product Descriptions |
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169 | (4) |
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7.2 Are Data Sheets Accurate and Complete? |
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173 | (2) |
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7.3 Selecting a Component That Is Fit for Use |
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175 | (1) |
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7.4 Using Data Sheets to Begin the Selection Process |
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176 | (2) |
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7.5 Construction Characteristics of Amplifiers and Switches |
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178 | (1) |
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7.6 Using Beta to Explain Device Tradeoffs |
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179 | (3) |
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7.7 Comparing Five BJTs to Illustrate Making a Selection |
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182 | (13) |
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7.8 Process for Making Tradeoffs |
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195 | (2) |
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7.9 Additional Choices for Picking a Component |
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197 | (1) |
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7.10 Thoughts About the Physical Design Examples |
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197 | (1) |
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198 | (1) |
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8 Selecting the Best Model for a Simulation |
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199 | (44) |
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8.1 From Component Choice to Model Choice |
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199 | (1) |
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8.2 Questions That Modeling and Simulation Can Answer |
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200 | (1) |
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201 | (1) |
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8.4 Using Symbols and Schematics to Represent Models |
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202 | (3) |
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8.5 Major Types of Models |
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205 | (6) |
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8.6 Compare Models by Simulation Performance |
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211 | (10) |
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8.7 Additional Model Comparisons |
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221 | (2) |
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8.8 Recommendations for Modeling |
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223 | (4) |
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8.9 Converting a Model to Another Type of Model |
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227 | (7) |
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8.10 Transform Models for Systems |
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234 | (7) |
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241 | (2) |
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9 Modeling and Simulation in the Design Process Flow |
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243 | (16) |
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9.1 Simulation in the Design Process |
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243 | (1) |
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9.2 A Typical Design Flow |
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244 | (4) |
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9.3 Strategy of Modeling and Simulation in Design |
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248 | (1) |
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9.4 Acquiring IBIS Models: An Overview |
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249 | (8) |
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257 | (2) |
PART 4: ABOUT THE IBIS MODEL |
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259 | (166) |
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10 Key Concepts of the IBIS Specification |
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261 | (54) |
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261 | (3) |
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264 | (19) |
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10.3 Sample IBIS Data File |
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283 | (11) |
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10.4 Parsing and Checking IBIS Data Files |
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294 | (3) |
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10.5 Schematic of a Basic IBIS Model |
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297 | (4) |
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10.6 How IBIS Circuit Modeling Methodology Is Used |
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301 | (8) |
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309 | (1) |
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10.8 ISO 9000 Process Documentation for IBIS Models |
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310 | (4) |
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314 | (1) |
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11 Using IBIS Models in What-If Simulations |
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315 | (46) |
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11.1 A New Method of Design and Development |
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315 | (1) |
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316 | (1) |
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11.3 Virtual Experiment Techniques |
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316 | (1) |
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11.4 Propagation Delay in High-Speed Nets |
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317 | (1) |
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11.5 Why We Use the IBIS Model |
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318 | (2) |
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11.6 Data Used in Experiments |
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320 | (2) |
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11.7 Experiment 1: Output Drive Capabity Versus Load |
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322 | (5) |
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11.8 Experiment 2: C_comp Loading |
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327 | (5) |
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11.9 All-Important Zo: Algorithms and Field Solvers |
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332 | (1) |
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11.10 Experiment 3: Edge Rate of a Driver and Reflections |
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333 | (3) |
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11.11 Experiment 4: Using V-T Data Versus a Ramp |
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336 | (10) |
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11.12 Experiment 5: Parasitics and Packaging Effects |
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346 | (3) |
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11.13 Experiment 6: Environmental and Population Variables |
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349 | (3) |
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11.14 Other Considerations: Timing and Noise Margin Issues |
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352 | (4) |
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11.15 Experiment 7: Vol from Simulation Versus Data Sheet |
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356 | (2) |
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11.16 How IBIS Handles Simulator Issues |
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358 | (1) |
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359 | (2) |
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12 Fixing Errors and Omissions in IBIS Models |
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361 | (34) |
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12.1 IBIS Model Validation Steps |
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361 | (1) |
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12.2 Process and Product Improvement Steps |
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362 | (1) |
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12.3 Step 1: Detect and Acknowledge the Quality Problem |
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363 | (1) |
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12.4 Step 2: Diagnose the Problem's Root Cause |
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364 | (2) |
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12.5 Step 3: Design a Fix Based on Root Cause |
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366 | (4) |
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12.6 Step 4: Verify the Fix |
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370 | (2) |
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12.7 Step 5: Archive Corrected Models |
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372 | (1) |
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12.8 Beyond Parsers and Checklists: Simulations and Reality Checking |
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372 | (2) |
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12.9 Tools Provided by the IBIS Committee |
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374 | (8) |
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12.10 IBIS Common Errors Checklist and Correction Procedures |
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382 | (4) |
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12.11 3Com's ISO 9000 Process for IBIS Models |
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386 | (5) |
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12.12 IBIS Model Acceptance and Legitimacy |
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391 | (3) |
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394 | (1) |
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13 Using EDA Tools to Create and Validate IBIS Models from SPICE |
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395 | (30) |
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395 | (1) |
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396 | (3) |
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13.3 SPICE-to-IBIS Conversion Methodology |
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399 | (15) |
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13.4 Modeling Passive Interconnections in IBIS |
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414 | (1) |
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13.5 IBIS Model Validation |
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415 | (7) |
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422 | (3) |
PART 5: MANAGING MODELS |
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425 | (52) |
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14 Sources of IBIS Models |
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427 | (26) |
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14.1 Model Needs Change as a Product is Developed |
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427 | (1) |
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14.2 List of IBIS Model Sources |
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428 | (2) |
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14.3 Using Default Models to Get Started |
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430 | (1) |
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14.4 Using the Company's Model Library |
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430 | (1) |
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14.5 Using the EDA Tool Provider's Model Library |
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430 | (1) |
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14.6 Searching the Web for the Supplier's Model |
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431 | (3) |
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14.7 Requesting Models Directly from the Supplier |
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434 | (2) |
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14.8 Purchasing a Commercial Third-Party Model Library |
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436 | (1) |
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14.9 Using Models Adapted from Other Models |
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437 | (3) |
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440 | (1) |
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14.11 Purchasing Custom Models from a Third-Party |
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441 | (1) |
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14.12 Converting SPICE Models to IBIS Models |
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441 | (1) |
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14.13 Using a Supplier's Preliminary Models |
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441 | (9) |
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14.14 Asking SI-List and IBIS E-mail Reflectors for Help |
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450 | (1) |
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14.15 Modeling Tools on the IBIS Website |
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451 | (1) |
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452 | (1) |
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15 Working with the Model Library |
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453 | (24) |
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15.1 The Best Way to Manage Models |
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453 | (5) |
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15.2 Component Standardization and Library Management |
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458 | (12) |
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15.3 Storing and Retrieving Model Files |
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470 | (3) |
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15.4 Assigning Models to Components in EDA Simulators |
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473 | (3) |
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15.5 Flexibility in Model Choices at Run Time |
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476 | (1) |
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476 | (1) |
PART 6: MODEL ACCURACY AND VERIFICATION |
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477 | (94) |
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16 Methodology for Verifying Models |
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479 | (32) |
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16.1 Overview of Model Verification |
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479 | (2) |
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16.2 Model Verification Methodology |
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481 | (8) |
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16.3 Verifying SPICE Models |
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489 | (8) |
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16.4 Verifying PDS Models |
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497 | (6) |
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16.5 Verifying IBIS Models |
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503 | (5) |
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16.6 Verifying Other Model Types |
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508 | (2) |
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510 | (1) |
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17 Verifying Model Accuracy by Using Laboratory Measurements |
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511 | (34) |
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511 | (1) |
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17.2 Instrumentation Loading as a Source of Errors |
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512 | (5) |
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17.3 Other Test Setup Errors |
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517 | (2) |
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17.4 Signal Noise as a Source of Errors |
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519 | (1) |
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17.5 Measurement Definitions and Terms as a Source of Errors |
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520 | (2) |
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17.6 Two Ways to Correlate Models with Measurements |
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522 | (1) |
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17.7 Involving Production in Verification |
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523 | (1) |
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523 | (1) |
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17.9 Correlating Unit-by-Unit Model Measurements |
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524 | (1) |
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17.10 Statistical Envelope Correlation |
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525 | (1) |
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17.11 Signal Integrity and Correlation |
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526 | (1) |
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17.12 Waveform Correlation |
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527 | (3) |
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17.13 Computational Electromagnetics and the Feature Selective Validation Method |
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530 | (4) |
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17.14 IBIS Golden Waveforms |
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534 | (1) |
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17.15 How Unexpected Errors Led to an Advance in Modeling |
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535 | (6) |
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17.16 Recommended Verification Strategy |
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541 | (3) |
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544 | (1) |
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18 Balancing Accuracy Against Practicality When Correlating Simulation Results |
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545 | (10) |
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18.1 Establishing Absolute Accuracy Is Difficult |
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545 | (2) |
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18.2 Is a Model Accurate Enough to Be Usable? |
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547 | (1) |
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18.3 Model Accuracy Definitions |
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547 | (1) |
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18.4 Confidence Limits in Measurements and Simulations |
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548 | (1) |
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18.5 How Much to Guard-Band Design Simulation? |
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549 | (1) |
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18.6 Differences in Accuracy, Dispersion, and Precision for Simulation and Measurement |
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550 | (1) |
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551 | (1) |
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18.8 Standardization and the Compact Model Council |
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551 | (3) |
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554 | (1) |
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19 Deriving an Equation-Based Model from a Macromodel |
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555 | (16) |
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19.1 A "New" RF Design Challenge |
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555 | (1) |
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555 | (1) |
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19.3 Applying the RF Example to High-Speed Digital Circuits |
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556 | (2) |
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19.4 Predicted and Measured Results |
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558 | (1) |
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19.5 Reverse Isolation Analyzed |
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559 | (7) |
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19.6 Optimizing Single-Stage Reverse Isolation |
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566 | (1) |
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19.7 Combining Stages for Power Isolation |
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567 | (2) |
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19.8 Calculations Versus Measurements |
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569 | (1) |
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19.9 Construction and Test Techniques |
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569 | (1) |
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570 | (1) |
PART 7: FUTURE DIRECTIONS IN MODELING |
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571 | (134) |
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573 | (58) |
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20.1 Emerging Simulation Requirements |
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573 | (3) |
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20.2 The Leading Contenders to Change IBIS |
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576 | (1) |
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20.3 Models in the Context of Simplification |
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577 | (1) |
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578 | (2) |
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580 | (8) |
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20.6 Developing a Macromodel from the Behavioral Model |
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588 | (4) |
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20.7 Developing a SPICE Macromodel from a Physical Model |
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592 | (16) |
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20.8 Limitations in Models Due to Simplification |
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608 | (2) |
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20.9 AMS Modeling Simplified |
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610 | (8) |
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20.10 Limitations Because of Parameter Variation |
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618 | (3) |
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20.11 Limitations of Deterministic Modeling and Design |
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621 | (8) |
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629 | (2) |
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21 Feedback to the Model Provider Improves Model Accuracy |
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631 | (10) |
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21.1 Continuing Need for Better Models |
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631 | (1) |
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21.2 How Far We Have Come |
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632 | (1) |
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21.3 Four-Step Universal Process for Improvement |
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633 | (1) |
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21.4 Specs That Swim Upstream: A New Approach |
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633 | (1) |
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21.5 Warnings About Doing What-If Model Simulations |
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634 | (1) |
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21.6 Selling the Idea of Better Models and Simulation |
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635 | (5) |
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640 | (1) |
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22 Future Trends in Modeling |
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641 | (42) |
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22.1 Bridges to the Future |
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641 | (1) |
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642 | (2) |
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22.3 How Design Methods Have Changed |
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644 | (1) |
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22.4 Attitudes in EMI/EMC about Modeling and Simulation |
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645 | (1) |
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22.5 High-Speed Design Is Becoming More Challenging |
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646 | (2) |
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22.6 Advantages of SPICE, S-Parameters, and IBIS |
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648 | (6) |
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22.7 Combining Models and EDA Tools to Design High-Speed Serial Busses |
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654 | (1) |
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22.8 IBIS: Past, Present, and Future Specification Additions |
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655 | (4) |
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22.9 Advantages of Pre-Layout Simulation for EMI/EMC |
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659 | (1) |
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22.10 Interconnection Design Applied to EMI/EMC |
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660 | (1) |
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22.11 Modeling for Power Integrity and EMI/EMC |
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661 | (10) |
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22.12 Computational Electromagnetics |
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671 | (5) |
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22.13 EDA Tool Supplier Survey |
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676 | (5) |
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22.14 Risk Management and the Limitations of Simulation |
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681 | (1) |
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681 | (2) |
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23 Using Probability: The Ultimate Future of Simulation Contributing author: Darren J. Carpenter, BT Exact |
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683 | (22) |
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683 | (2) |
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23.2 Limitations of Deterministic Modeling and Design |
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685 | (2) |
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23.3 A New Approach: Probabilistic Modeling |
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687 | (1) |
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23.4 Complexity of the EMI Chain of Cause and Effect |
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688 | (1) |
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23.5 Risk Management Mathematics |
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689 | (3) |
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23.6 Identical Equipments Case |
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692 | (1) |
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23.7 Non-Identical Equipments Case |
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693 | (1) |
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693 | (1) |
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23.9 Distribution Examples |
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694 | (7) |
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23.10 Review of Probability Distributions |
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701 | (1) |
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23.11 Follow Up Simulation with Product Assurance |
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702 | (1) |
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703 | (2) |
PART 8: GLOSSARY, BIBLIOGRAPHY, INDEX, AND CD-ROM |
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705 | |
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707 | (26) |
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733 | (12) |
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745 | |
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Using the Companion CD-ROM |
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