Automatic clustering of wafer spatial signatures

Wangyang Zhang, Xin Li, Sharad Saxena, Andrzej Strojwas, Rob Rutenbar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 50th Annual Design Automation Conference, DAC 2013
DOIs
StatePublished - 2013
Event50th Annual Design Automation Conference, DAC 2013 - Austin, TX, United States
Duration: May 29 2013Jun 7 2013

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Other

Other50th Annual Design Automation Conference, DAC 2013
CountryUnited States
CityAustin, TX
Period5/29/136/7/13

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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