Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

Katherine Shu Min Li, Leon Li-Yang Chen, Ken Chau Cheung Cheng, Peter Yi-Yu Liao, Sying Jyan Wang, Andrew Yi An Huang, Nova Tsai, Leon Chou, Gus Chang Hung Han, Jwu E. Chen, Hsing Chung Liang, Chun Lung Hsu

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

1 Scopus citations

Abstract

we propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE European Test Symposium, ETS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665418492
DOIs
StatePublished - 24 May 2021
Event26th IEEE European Test Symposium, ETS 2021 - Virtual, Bruges, Belgium
Duration: 24 May 202128 May 2021

Publication series

NameProceedings of the European Test Workshop
Volume2021-May
ISSN (Print)1530-1877
ISSN (Electronic)1558-1780

Conference

Conference26th IEEE European Test Symposium, ETS 2021
Country/TerritoryBelgium
CityVirtual, Bruges
Period24/05/2128/05/21

Keywords

  • clustering
  • defect pattern
  • machine learning
  • mask
  • wafermap
  • yield learning

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