TestDNA-E: Wafer Defect Signature for Pattern Recognition by Ensemble Learning

Wafer failure pattern recognition can be used for root cause analysis, which is very important for yield learning. Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2022-05, Vol.35 (2), p.372-374
Main Authors: Li, Katherine Shu-Min, Chen, Leon Li-Yang, Cheng, Ken Chau-Cheung, Liao, Peter Yi-Yu, Wang, Sying-Jyan, Huang, Andrew Yi-Ann, Chou, Leon, Tsai, Nova Cheng-Yen, Lee, Chen-Shiun
Format: Article
Language:English
Subjects:
Algorithms
Classification
Decision trees
Ensemble learning
Failure analysis
Feature extraction
Machine learning
Machine learning algorithms
Pattern recognition
Prediction algorithms
Root cause analysis
Spatial data
Sports
test probe
Transforms
wafer defect diagnosis
wafer defect map
wafer defect pattern
Wafer test
yield learning
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Summary:Wafer failure pattern recognition can be used for root cause analysis, which is very important for yield learning. Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve good classification results. In this letter, we propose to enhance the classification accuracy with the help of spatial information and ensemble learning algorithms. Experimental results indicate that the proposed method can further improve the accuracy by 8.9%.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2022.3145855