Automatic clustering of wafer spatial signatures

Published

Conference Paper

In this paper, we propose a methodology based on unsupervised learning for automatic clustering of wafer spatial signatures to aid yield improvement. Our proposed methodology is based on three steps. First, we apply sparse regression to automatically capture wafer spatial signatures by a small number of features. Next, we apply an unsupervised hierarchical clustering algorithm to divide wafers into a few clusters where all wafers within the same cluster are similar. Finally, we develop a modified L-method to determine the appropriate number of clusters from the hierarchical clustering result. The accuracy of the proposed methodology is demonstrated by several industrial data sets of silicon measurements. Copyright © 2013 ACM.

Full Text

Duke Authors

Cited Authors

  • Zhang, W; Li, X; Saxena, S; Strojwas, A; Rutenbar, R

Published Date

  • July 12, 2013

Published In

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781450320719

Digital Object Identifier (DOI)

  • 10.1145/2463209.2488821

Citation Source

  • Scopus