Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

32 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)228-240
Journal / PublicationEuropean Journal of Operational Research
Volume190
Issue number1
Publication statusPublished - 1 Oct 2008
Externally publishedYes

Abstract

Defects on semiconductor wafers tend to cluster and the spatial defect patterns contain useful information about potential problems in the manufacturing process. This study proposes to use model-based clustering algorithms via Bayesian inferences for spatial defect pattern recognition on semiconductor wafers. These new algorithms can find the number of defect clusters as well as identify the pattern of each cluster automatically. They are capable of detecting curvilinear patterns, ellipsoidal patterns and nonuniform global defect patterns. Promising results have been obtained from simulation studies. © 2007 Elsevier B.V. All rights reserved.

Research Area(s)

  • Pattern recognition, Quality control, Semiconductor manufacturing, Stochastic processes