Efficient spatial pattern analysis for variation decomposition via robust sparse regression

Published

Journal Article

In this paper, we propose a new technique to achieve accurate decomposition of process variation by efficiently performing spatial pattern analysis. We demonstrate that the spatially correlated systematic variation can be accurately represented by the linear combination of a small number of templates. Based on this observation, an efficient sparse regression algorithm is developed to accurately extract the most adequate templates to represent spatially correlated variation. In addition, a robust sparse regression algorithm is proposed to automatically remove measurement outliers. We further develop a fast numerical algorithm that may reduce the computational time by several orders of magnitude over the traditional direct implementation. Our experimental results based on both synthetic and silicon data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy and efficiency. © 1982-2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhang, W; Balakrishnan, K; Li, X; Boning, DS; Saxena, S; Strojwas, A; Rutenbar, RA

Published Date

  • July 15, 2013

Published In

Volume / Issue

  • 32 / 7

Start / End Page

  • 1072 - 1085

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2013.2245942

Citation Source

  • Scopus