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Novel Algorithms for Fast Statistical Analysis of Scaled Circuits

  • Authors
  • Amith Singhee
  • Rob A. Rutenbar

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 46)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Amith Singhee, Rob A. Rutenbar
    Pages 1-57
  3. Amith Singhee, Rob A. Rutenbar
    Pages 59-122
  4. Amith Singhee, Rob A. Rutenbar
    Pages 123-169
  5. Amith Singhee, Rob A. Rutenbar
    Pages 171-173
  6. Back Matter
    Pages 175-195

About this book

Introduction

As VLSI technology moves to the nanometer scale for transistor feature sizes, the impact of manufacturing imperfections result in large variations in the circuit performance. Traditional CAD tools are not well-equipped to handle this scenario, since they do not model this statistical nature of the circuit parameters and performances, or if they do, the existing techniques tend to be over-simplified or intractably slow. Novel Algorithms for Fast Statistical Analysis of Scaled Circuits draws upon ideas for attacking parallel problems in other technical fields, such as computational finance, machine learning and actuarial risk, and synthesizes them with innovative attacks for the problem domain of integrated circuits. The result is a set of novel solutions to problems of efficient statistical analysis of circuits in the nanometer regime. In particular, Novel Algorithms for Fast Statistical Analysis of Scaled Circuits makes three contributions:

1) SiLVR, a nonlinear response surface modeling and performance-driven dimensionality reduction strategy, that automatically captures the designer’s insight into the circuit behavior, by extracting quantitative measures of relative global sensitivities and nonlinear correlation.

2) Fast Monte Carlo simulation of circuits using quasi-Monte Carlo, showing speedups of 2× to 50× over standard Monte Carlo.

3) Statistical blockade, an efficient method for sampling rare events and estimating their probability distribution using limit results from extreme value theory, applied to high replication circuits like SRAM cells.

Keywords

Monte Carlo Scaled Circuits Statistical Analysis VLSI VLSI circuits algorithms derivation extreme value modeling simulation

Bibliographic information

  • DOI https://doi.org/10.1007/978-90-481-3100-6
  • Copyright Information Springer Netherlands 2009
  • Publisher Name Springer, Dordrecht
  • eBook Packages Engineering
  • Print ISBN 978-90-481-3099-3
  • Online ISBN 978-90-481-3100-6
  • Series Print ISSN 1876-1100
  • Series Online ISSN 1876-1119
  • Buy this book on publisher's site