Recognition of semiconductor defect patterns using spectral clustering

Chih-Hsuan Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Diverse defect patterns shown on the wafer map usually contain important information for quality engineers to find their root causes of abnormalities. Today, even with highly automated and precisely monitored facilities used in a near dust-free clean room and operated with well-trained process engineers, the occurrence of spatial defects still cannot be avoided. This research presents a spatial defect diagnosis system and attempts to solve two challenging problems for semiconductor manufacturing: (1) To estimate the number of defect clusters in advance, and (2) To separate both convex and non-convex defect clusters at the same time. In this paper, a spatial filter is used to denoise the noisy wafer bin map (WBM) and to extract meaningful defect clusters. To isolate various types of defect patterns, a hybrid scheme combining entropy fuzzy c means (EFCM) with spectral clustering is applied to the denoised output. The proposed approach is validated with an empirical wafer bin maps obtained in a DRAM company in Taiwan. Experimental results show that four kinds of mixed-type defect patterns are successfully extracted and classified.

Original languageEnglish
Title of host publicationIEEM 2007
Subtitle of host publication2007 IEEE International Conference on Industrial Engineering and Engineering Management
Pages587-591
Number of pages5
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2007 - , Singapore
Duration: 2 Dec 20074 Dec 2007

Publication series

NameIEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management

Conference

Conference2007 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2007
CountrySingapore
Period2/12/074/12/07

Keywords

  • Data mining
  • Defect pattern
  • Fuzzy clustering
  • Spectral clustering

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