Spatial variation decomposition via sparse regression

Wangyang Zhang, Karthik Balakrishnan, Xin Li, Duane Boning, Emrah Acar, Frank Liu, Robin A Rutenbar

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

Abstract

In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims 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 the spatially correlated variation can be accurately represented by the linear combination of a small number of "templates". Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.

Original languageEnglish (US)
Title of host publicationICICDT 2012 - IEEE International Conference on Integrated Circuit Design and Technology
DOIs
StatePublished - Aug 13 2012
EventIEEE International Conference on Integrated Circuit Design and Technology, ICICDT 2012 - Austin, TX, United States
Duration: May 30 2012Jun 1 2012

Publication series

NameICICDT 2012 - IEEE International Conference on Integrated Circuit Design and Technology

Other

OtherIEEE International Conference on Integrated Circuit Design and Technology, ICICDT 2012
CountryUnited States
CityAustin, TX
Period5/30/126/1/12

Keywords

  • integrated circuit
  • process variation
  • variation decomposition

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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