Enhancing Good-Die-in-Bad-Neighborhood Methodology with Wafer-Level Defect Pattern Information

Ching Min Liu, Chia Heng Yen, Shu Wen Lee, Kai Chiang Wu, Mango Chia Tso Chao

研究成果: Conference contribution同行評審


In semiconductor manufacturing processes, there are several causes of typical defects in silicon wafers, such as operational flaws or equipment malfunctions, which may lead to circuit failure and defective products. Therefore, testing is instrumental in improving overall yield and reliability. GDBN (good die in bad neighborhood) is a widely-used technique of rejecting potentially defective wafers in advance based on the concept that defects tend to cluster together. However, previous studies related to GDBN are limited to a local observation by using a narrow-sighted window and thus ignore the defects patterns of the wafers. In this paper, by leveraging information of wafer defect patterns and extending the observation range to the entire wafer, we strengthen the GDBN method to recognize the potentially defective dice more effectively based on the feature of the different defect patterns. The method proposed in this paper is realized by convolutional neural network technology, and it is also the first method to consider defect patterns of wafers as features to solve the problem of GDBN. Several experiments are conducted on a real-world WM-811K dataset, and the results show that our proposed method not only reduce the cost of return merchandise authorization (RMA) but the DPPM (Defective Parts Per Million) more significantly over other existing methods.

主出版物標題Proceedings - 2023 IEEE International Test Conference, ITC 2023
發行者Institute of Electrical and Electronics Engineers Inc.
出版狀態Published - 2023
事件2023 IEEE International Test Conference, ITC 2023 - Anaheim, United States
持續時間: 7 10月 202315 10月 2023


名字Proceedings - International Test Conference


Conference2023 IEEE International Test Conference, ITC 2023
國家/地區United States


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