Machine Learning-Based Detection Method for Wafer Test Induced Defects

Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies. In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield. However, the wafer test itself may induce defects to otherwise good dies. Test-induced de...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2021-05, Vol.34 (2), p.161-167
Main Authors: Cheng, Ken Chau-Cheung, Chen, Leon Li-Yang, Li, Ji-Wei, Li, Katherine Shu-Min, Tsai, Nova Cheng-Yen, Wang, Sying-Jyan, Huang, Andrew Yi-Ann, Chou, Leon, Lee, Chen-Shiun, Chen, Jwu E., Liang, Hsing-Chung, Hsu, Chun-Lung
Format: Article
Language:English
Subjects:
Acceptance tests
Algorithms
Defects
Fabrication
Fault diagnosis
Integrated circuits
Machine learning
Machine learning algorithms
Needles
site-dependent fault
test yield
test-induced defects
Testing
Very large scale integration
wafer map
Wafer test
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Summary:Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies. In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield. However, the wafer test itself may induce defects to otherwise good dies. Test-induced defects not only hurt overall manufacturing yield but also create problems for yield learning, so the source problems in testing should be identified quickly. In the wafer acceptance test process, dies are probed in a predetermined order, so test-induced defects, also known as site-dependent faults, exhibit specific patterns that can be effectively captured in test paths. In this paper, we analyze characteristics of test-induced defect patterns and define features that can be used by machine learning algorithms for the automatic detection of test-induced defects. Therefore, defective dies caused by the wafer test can be retested for yield improvement. Test data from six real products are used to validate the proposed method. Several machine learning algorithms have been applied, and experimental results show that our method is effective to distinguish between test-induced and fabrication-induced defects. On average, the prediction accuracy is higher than 97%.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2021.3065405