Machine Learning-Based Detection Method for Wafer Test Induced Defects

Ken Chau Cheung Cheng, Leon Li Yang Chen, Ji Wei Li, Katherine Shu Min Li, Nova Cheng Yen Tsai, Sying Jyan Wang, Andrew Yi Ann Huang, Leon Chou, Chen Shiun Lee, Jwu E. Chen, Hsing Chung Liang, Chun Lung Hsu

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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%.

Original languageEnglish
Article number9375473
Pages (from-to)161-167
Number of pages7
JournalIEEE Transactions on Semiconductor Manufacturing
Issue number2
StatePublished - May 2021


  • Wafer test
  • machine learning
  • site-dependent fault
  • test yield
  • test-induced defects
  • wafer map


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