Semi-Supervised Framework for Wafer Defect Pattern Recognition with Enhanced Labeling

Leon Li Yang Chen, Katherine Shu-Min Li, Xu Hao Jiang, Sying Jyan Wang, Andrew Yi Ann Huang, Jwu E. Chen, Hsing Chung Liang, Chun Lung Hsu

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

2 Scopus citations

Abstract

Wafer map defect pattern recognition is valuable for root cause analysis and yield learning. Most of the previous studies on defect pattern recognition are based on supervised machine learning, in which labeled wafer maps are used to train a machine learning model for automatic classification. Some problems arise in this approach. First, there may be misclassification in the original labeled data, which makes it difficult to establish an accurate prediction model. Secondly, defect patterns that are not defined before will not be classified correctly. In this paper, we proposed a semi-supervised framework to deal with these problems. Labeled wafer maps are first used to train a prediction model, with likely misclassified data excluded. The prediction model is then used to classify unlabeled data. The remaining data that cannot be properly classified are then sent to an unsupervised learning algorithm to extract more defect patterns with enhanced labeling techniques. This proposed approach is validated with TSMC 811K database, in which we are able to define five new defect pattern types. Experimental results show that total 14 defect types can be recognized with overall accuracy of 94.37%.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Test Conference, ITC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages208-212
Number of pages5
ISBN (Electronic)9781665416955
DOIs
StatePublished - 2021
Event2021 IEEE International Test Conference, ITC 2021 - Virtual, Online, United States
Duration: 10 Oct 202115 Oct 2021

Publication series

NameProceedings - International Test Conference
ISSN (Print)1089-3539

Conference

Conference2021 IEEE International Test Conference, ITC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/10/2115/10/21

Keywords

  • defect pattern
  • enhanced labeling
  • machine learning
  • unsupervised machine learning
  • wafer map
  • yield learning

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