Spatial variation decomposition via sparse regression

Published

Conference Paper

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. © 2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhang, W; Balakrishnan, K; Li, X; Boning, D; Acar, E; Liu, F; Rutenbar, RA

Published Date

  • August 13, 2012

Published In

  • Icicdt 2012 Ieee International Conference on Integrated Circuit Design and Technology

International Standard Book Number 13 (ISBN-13)

  • 9781467301466

Digital Object Identifier (DOI)

  • 10.1109/ICICDT.2012.6232875

Citation Source

  • Scopus