Spatial variation decomposition via sparse regression
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spatial_icicdt ver09.pdf
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1.62 MB
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Author(s)
Zhang, Wangyang
Balakrishnan, Karthik
Li, Xin
Acar, Emrah
Liu, Frank
Rutenbar, Rob A.
Boning, Duane S.
Date Issued
May 2012
Journal
Proceedings of the 2012 IEEE International Conference on IC Design & Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Wangyang, Karthik Balakrishnan, Xin Li, Duane Boning, Emrah Acar, Frank Liu, and Rob A. Rutenbar. “Spatial Variation Decomposition via Sparse Regression.” 2012 IEEE International Conference on IC Design & Technology (May 2012).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICICDT.2012.6232875
