Efficient spatial variation modeling via robust dictionary learning

Published

Conference Paper

© 2016 EDAA. In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative coordinate descent method is further introduced to solve the dictionary learning problem with consideration of missing data. Our numerical experiments based on industrial measurement data demonstrate that the proposed method achieves up to 70% error reduction over the conventional VP approach without increasing the measurement cost.

Full Text

Duke Authors

Cited Authors

  • Liao, C; Tao, J; Zeng, X; Su, Y; Zhou, D; Li, X

Published Date

  • April 25, 2016

Published In

  • Proceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, Date 2016

Start / End Page

  • 121 - 126

International Standard Book Number 13 (ISBN-13)

  • 9783981537062

Digital Object Identifier (DOI)

  • 10.3850/9783981537079_0074

Citation Source

  • Scopus