Semantic Segmentation-Based Wafer Map Mixed-Type Defect Pattern Recognition
Jinda Yan, Yi Sheng, Minghao Piao
Abstract
Recent research applying deep learning to the field of defect pattern recognition in wafer maps has greatly accelerated the process of defect detection. However, when different defects are mixed on the same wafer, the mixed type is very complex, and it is still difficult to recognize the defect pattern. In this article, we propose a new framework to segment different defect patterns on the wafer map by using a semantic segmentation approach. This method works well on single and known and unknown mixed types. First, we extract the defects from the single-defect wafer map of the MixedWM38 dataset to generate single-defect pixel-level labels. Then, a mixed defect pattern dataset suitable for semantic segmentation is generated using single-defect wafer maps and single-defect pixel-level labels. The average accuracy of the test set on our synthetic dataset reaches over 97%, and using the trained model for testing on MixedWM38, we can get an average accuracy of 95.8%.