Unsupervised Wafer Defect Classification Model Based On Joint Reconstruction And Clustering

Abstract

Defect detection of unpatterned wafer is very important for determining the causes of wafer defects, and it is also a significant way to improve production yield. At present, the defect detection model based on the deep learning method has been widely used and has shown promising performance. However, the labor cost of supervised learning method based on labeled samples is very expensive, so the wafer defect detection model based on unsupervised learning is a future research direction. In this paper, we propose an unsupervised wafer defect classification model(UWDDM), in which the reconstruction loss is used to train the feature extractor of wafer defects, and the classifier is trained based on the cross entropy loss of clustering results and classification results. The experimental results on the real dataset MixedWM38 show that the proposed model has higher recognition accuracy and less inference time than other unsupervised models. This unsupervised learning method has great potential for wafer defect detection. It can avoid the expensive manual labeling cost and provide an effective way to automatically detect wafer defects.