Variation-Aware Analog Structural Synthesis
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 top
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ANALOG CIRCUITS AND SIGNAL PROCESSING SERIES Consulting Editor: Mohammed Ismail. Ohio State University
For other titles published in this series, go to www.springer.com/series/7381
Trent McConaghy • Pieter Palmers Michiel Steyaert • Georges Gielen
•
Peng Gao
Variation-Aware Analog Structural Synthesis A Computational Intelligence Approach
123
Dr. Trent McConaghy Solido Design Automation, Inc. 102-116 Research Drive Saskatoon SK S7N 3R3 Canada [email protected] Dr. Pieter Palmers Mephisto Design Automation NV (MDA) Romeinsestraat 18 3001 Heverlee Belgium Peng Gao Katholieke Universiteit Leuven Department of Electrical Engineering (ESAT) Kasteelpark Arenberg 10 3001 Leuven Belgium
Prof. Michiel Steyaert Katholieke Universiteit Leuven Department of Electrical Engineering (ESAT) Kasteelpark Arenberg 10 3001 Leuven Belgium [email protected] Prof. Georges Gielen Katholieke Universiteit Leuven Department of Electrotechnical Engineering Div. Microelectronics & Sensors (MICAS) Kasteelpark Arenberg 10 3001 Leuven Belgium [email protected]
ISSN ISBN 978-90-481-2905-8 e-ISBN 978-90-481-2906-5 DOI 10.1007/978-90-481-2906-5 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009927593 c Springer Science+Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Summary of Contents
Contents
Preface
xi
Acronyms and Notation
xv
1.
. . . . .
1 1 4 17 24 24
2.
Variation-Aware Sizing: Background 2.1 Introduction and Problem Formulation . . . . . . . . . . . . 2.2 Review of Yield Optimization Approaches . . . . . . . . . . 2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . .
27 27 32 44
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 . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .
47 47 48 53 59 59 70 82 83
Knowledge Extraction in Sizing: CAFFEINE 4.1 Introduction and Problem Formulation . . . . . . . . . . . . 4.2 Background: GP and Symbolic Regression . . . . . . . . . .
85 85 90
4.
Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . 1.2 Background and Contributions to Analog CAD 1.3 Background and Contributions to AI . . . .
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