A framework for accounting for process model uncertainty in statistical static timing analysis

Yu Guo, Dong Wei, Feng Zhuo, Li Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

In recent years, a large body of statistical static timing analysis and statistical circuit optimization techniques have emerged, providing important avenues to account for the increasing process variations in design. The realization of these statistical methods often demands the availability of statistical process variation models whose accuracy, however, is severely hampered by limitations in test structure design, test time and various sources of inaccuracy inevitably incurred in process characterization. Consequently, it is desired that statistical circuit analysis and optimization can be conducted based upon imprecise statistical variation models. In this paper, we present an efficient importance sampling based optimization framework that can translate the uncertainty in the process models to the uncertainty in parametric yield, thus offering the very much desired statistical best/worst-case circuit analysis capability accounting for unavoidable complexity in process characterization. Unlike the previously proposed statistical learning and probabilistic interval based techniques, our new technique efficiently computes tight bounds of the parametric circuit yields based upon bounds of statistical process model parameters while fully capturing correlation between various process variations. Furthermore, our new technique provides valuable guidance to process characterization. Examples are included to demonstrate the application of our general analysis framework under the context of statistical static timing analysis.

Original languageEnglish
Title of host publication2007 44th ACM/IEEE Design Automation Conference, DAC'07
Pages829-834
Number of pages6
DOIs
StatePublished - 2007
Event2007 44th ACM/IEEE Design Automation Conference, DAC'07 - San Diego, CA, United States
Duration: 4 Jun 20078 Jun 2007

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference2007 44th ACM/IEEE Design Automation Conference, DAC'07
Country/TerritoryUnited States
CitySan Diego, CA
Period4/06/078/06/07

Keywords

  • Importance sampling
  • SSTA
  • Yield

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