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

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.