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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)


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%.

主出版物標題Proceedings - 2021 IEEE International Test Conference, ITC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
出版狀態已出版 - 2021
事件2021 IEEE International Test Conference, ITC 2021 - Virtual, Online, United States
持續時間: 10 10月 202115 10月 2021


名字Proceedings - International Test Conference


???event.eventtypes.event.conference???2021 IEEE International Test Conference, ITC 2021
國家/地區United States
城市Virtual, Online


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