Toward efficient spatial variation decomposition via sparse regression

Wangyang Zhang, Karthik Balakrishnan, Xin Li, Duane Boning, Rob Rutenbar

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

Abstract

In this paper, we propose a new technique to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that spatially correlated variation carries a unique sparse signature in frequency domain. Based upon this observation, an efficient sparse regression algorithm is applied to accurately separate spatially correlated variation from uncorrelated random variation. An important contribution of this paper is to develop a fast numerical algorithm that reduces the computational time of sparse regression by several orders of magnitude over the traditional implementation. Our experimental results based on silicon measurement data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy. The estimation error is reduced by more than 3.5 compared to other traditional methods.

Original languageEnglish (US)
Title of host publication2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
Pages162-169
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011 - San Jose, CA, United States
Duration: Nov 7 2011Nov 10 2011

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Other

Other2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
CountryUnited States
CitySan Jose, CA
Period11/7/1111/10/11

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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