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Efficient spatial pattern analysis for variation decomposition via robust sparse regression

Publication ,  Journal Article
Zhang, W; Balakrishnan, K; Li, X; Boning, DS; Saxena, S; Strojwas, A; Rutenbar, RA
Published in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
July 15, 2013

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.

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Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

July 15, 2013

Volume

32

Issue

7

Start / End Page

1072 / 1085

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering
 

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Zhang, W., Balakrishnan, K., Li, X., Boning, D. S., Saxena, S., Strojwas, A., & Rutenbar, R. A. (2013). Efficient spatial pattern analysis for variation decomposition via robust sparse regression. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 32(7), 1072–1085. https://doi.org/10.1109/TCAD.2013.2245942
Zhang, W., K. Balakrishnan, X. Li, D. S. Boning, S. Saxena, A. Strojwas, and R. A. Rutenbar. “Efficient spatial pattern analysis for variation decomposition via robust sparse regression.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 32, no. 7 (July 15, 2013): 1072–85. https://doi.org/10.1109/TCAD.2013.2245942.
Zhang W, Balakrishnan K, Li X, Boning DS, Saxena S, Strojwas A, et al. Efficient spatial pattern analysis for variation decomposition via robust sparse regression. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2013 Jul 15;32(7):1072–85.
Zhang, W., et al. “Efficient spatial pattern analysis for variation decomposition via robust sparse regression.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 7, July 2013, pp. 1072–85. Scopus, doi:10.1109/TCAD.2013.2245942.
Zhang W, Balakrishnan K, Li X, Boning DS, Saxena S, Strojwas A, Rutenbar RA. Efficient spatial pattern analysis for variation decomposition via robust sparse regression. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2013 Jul 15;32(7):1072–1085.

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

July 15, 2013

Volume

32

Issue

7

Start / End Page

1072 / 1085

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering