Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach by Trent McConaghyVariation-Aware Analog Structural Synthesis: A Computational Intelligence Approach by Trent McConaghy

Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach

byTrent McConaghy

Paperback | November 29, 2011

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Variation-Aware Analog Structural Synthesis describes computational intelligence-based tools for robust design of analog circuits. It starts with global variation-aware sizing and knowledge extraction, and progressively extends to variation-aware topology design. The computational intelligence techniques developed in this book generalize beyond analog CAD, to domains such as robotics, financial engineering, automotive design, and more.
Trent McConaghy is co-founder and Chief Scientific Officer of Solido Design Automation Inc. He was a co-founder and Chief Scientist of Analog Design Automation Inc., which was acquired by Synopsys Inc. in 2004. Prior to that, he did research for the Canadian Department of National Defense. He received his PhD degree in Electrical Engin...
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Title:Variation-Aware Analog Structural Synthesis: A Computational Intelligence ApproachFormat:PaperbackDimensions:328 pages, 9.25 × 6.1 × 0.68 inPublished:November 29, 2011Publisher:Springer NetherlandsLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9400726082

ISBN - 13:9789400726086

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Table of Contents

Preface. Acronyms and Notation.1. INTRODUCTION. 1.1 Motivation. 1.2 Background and Contributions to Analog CAD. 1.3 Background and Contributions to AI. 1.4 Analog CAD Is a Fruitfly for AI. 1.5 Conclusion.2. VARIATION-AWARE SIZING: BACKGROUND. 2.1 Introduction and Problem Formulation. 2.2 Review of Yield Optimization Approaches. 2.3 Conclusion.3. GLOBALLY RELIABLE, VARIATION-AWARE SIZING: SANGRIA. 3.1 Introduction. 3.2 Foundations: Model-Building Optimization (MBO). 3.3 Foundations: Stochastic Gradient Boosting. 3.4 Foundations: Homotopy. 3.5 SANGRIA Algorithm. 3.6 SANGRIA Experimental Results. 3.7 On Scaling to Larger Circuits. 3.8 Conclusion.4. KNOWLEDGE EXTRACTION IN SIZING: CAFFEINE. 4.1 Introduction and Problem Formulation. 4.2 Background: GP and Symbolic Regression. 4.3 CAFFEINE Canonical Form Functions. 4.4 CAFFEINE Search Algorithm. 4.5 CAFFEINE Results. 4.6 Scaling Up CAFFEINE: Algorithm. 4.7 Scaling Up CAFFEINE: Results. 4.8 Application: Behaviorial Modeling. 4.9 Application: Process-Variable Robustness Modeling. 4.10 Application: Design-Variable Robustness Modeling. 4.11 Application: Automated Sizing. 4.12 Application: Analytical Performance Tradeoffs. 4.13 Sensitivity To Search Algorithm. 4.14 Conclusion.5. CIRCUIT TOPOLOGY SYNTHESIS: BACKGROUND. 5.1 Introduction. 5.2 Topology-Centric Flows. 5.3 Reconciling System-Level Design. 5.4 Requirements for a Topology Selection / Design Tool. 5.5 Open-Ended Synthesis and the Analog Problem Domain. 5.6 Conclusion.6. TRUSTWORTHY TOPOLOGY SYNTHESIS: MOJITO SEARCH SPACE. 6.1 Introduction. 6.2 Search Space Framework. 6.3 A Highly Searchable Op Amp Library. 6.4 Operating-Point Driven Formulation. 6.5 Worked Example. 6.6 Size of Search Space. 6.7 Conclusion.7. TRUSTWORTHY TOPOLOGY SYNTHESIS: MOJITO ALGORITHM. 7.1 Introduction. 7.2 High-Level Algorithm. 7.3 Search Operators. 7.4 Handling Multiple Objectives. 7.5 Generation of Initial Individuals. 7.6 Experimental Setup. 7.7 Experiment: Hit Target Topologies? 7.8 Experiment: Diversity? 7.9 Experiment: Human-Competitive Results? 7.10 Discussion: Comparison to Open-Ended Structural Synthesis. 7.11 Conclusion.8. KNOWLEDGE EXTRACTION IN TOPOLOGY SYNTHESIS. 8.1 Introduction. 8.2 Generation of Database. 8.3 Extraction of Specs-To-Topology Decision Tree. 8.4 Global Nonlinear Sensitivity Analysis. 8.5 Extraction of Analytical Performance Tradeoffs. 8.6 Conclusion. 9. VARIATION-AWARE TOPOLOGY SYNTHESIS & KNOWLEDGE EXTRACTION. 9.1 Introduction. 9.2 Problem Specification. 9.3 Background. 9.4 Towards a Solution. 9.5 Proposed Approach: MOJITO-R. 9.6 MOJITO-R Experimental Validation. 9.7 Conclusion.10. NOVEL VARIATION-AWARE TOPOLOGY SYNTHESIS. 10.1 Introduction. 10.2 Background. 10.3 MOJITO-N Algorithm and Results. 10.4 ISCLEs Algorithm And Results. 10.5 Conclusion.11. CONCLUSION. 11.1 General Contributions. 11.2 Specific Contributions. 11.3 Future Work. 11.4 Final Remarks.References. Index.

Editorial Reviews

From the reviews:"This book is squarely aimed at analog circuit designers who are searching for new approaches to analog structural design and optimization. . Those most likely to benefit from this book . are experts in the field of industrial circuit design seeking insight into new design tools. . for the non-expert, with only a cursory understanding of the background material, the processes and results described in this book are an inspiring example of real-world applications of evolutionary design." (John Rieffel, Genetic Programming and Evolvable Machines, Vol. 12, 2011)