Automatic clustering of wafer spatial signatures

Authors:
Wangyang Zhang;Xin Li;Sharad Saxena;Andrzej Strojwas;Rob Rutenbar
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;PDF Solutions, Renner Trail, Richardson, TX;Carnegie Mellon University, Pittsburgh, PA;University of Illinois at Urbana-Champaign, Urbana IL
Venue:
Proceedings of the 50th Annual Design Automation Conference
Year:
2013

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Abstract

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.