Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach / Edition 1

Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach / Edition 1

by Trent McConaghy, Pieter Palmers, Gao Peng, Michiel Steyaert
     
 

ISBN-10: 9048129052

ISBN-13: 9789048129058

Pub. Date: 07/21/2009

Publisher: Springer Netherlands

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

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Overview

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.

Product Details

ISBN-13:
9789048129058
Publisher:
Springer Netherlands
Publication date:
07/21/2009
Series:
Analog Circuits and Signal Processing Series
Edition description:
2009
Pages:
305
Product dimensions:
9.21(w) x 6.14(h) x 0.75(d)

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: Shastic 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.

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