Exploiting correlation kernels for efficient handling of intra-die spatial correlation, with application to statistical timing

Amith Singhee, Sonia Singhal, Rob A. Rutenbar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Intra-die manufacturing variations are unavoidable in nanoscale processes. These variations often exhibit strong spatial correlation. Standard grid-based models assume model parameters (grid-size, regularity) in an ad hoc manner and can have high measurement cost. The random £eld model overcomes these issues. However, no general algorithm has been proposed for the practical use of this model in statistical CAD tools. In this paper, we propose a robust and ef£cient numerical method, based on the Galerkin technique and Karhunen Loéve Expansion, that enables effective use of the model. We test the effectiveness of the technique using a Monte Carlo-based Statistical Static Timing Analysis algorithm, and see errors less than 0.7%, while reducing the number of random variables from thousands to 25, resulting in speedups of up to 100x.

Original languageEnglish (US)
Title of host publicationDesign, Automation and Test in Europe, DATE 2008
Pages856-861
Number of pages6
DOIs
StatePublished - 2008
Externally publishedYes
EventDesign, Automation and Test in Europe, DATE 2008 - Munich, Germany
Duration: Mar 10 2008Mar 14 2008

Other

OtherDesign, Automation and Test in Europe, DATE 2008
CountryGermany
CityMunich
Period3/10/083/14/08

ASJC Scopus subject areas

  • Engineering(all)

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