Discriminative feature learning and cluster-based defect label reconstruction for reducing uncertainty in wafer bin map labels

Many studies have been conducted to improve wafer bin map (WBM) defect classification performance because accurate WBM classification can provide information about abnormal processes causing a decrease in yield. However, in the actual manufacturing field, the manual labeling performed by engineers leads to a high level of uncertainty. Label uncertainty has been a major cause of the reduction in WBM classification system performance. In this paper, we propose a class label reconstruction method for subdividing a defect class with various patterns into several groups, creating a new class for defect samples that cannot be categorized into known classes and detecting unknown defects. The proposed method performs discriminative feature learning of the Siamese network and repeated cross-learning of the class label reconstruction based on Gaussian means clustering in a learned feature space. We verified the proposed method using a real-world WBM dataset. In a situation where there the class labels of the training dataset were corrupted, the proposed method could increase the classification accuracy of the test dataset by enabling the corrupted sample to find its original class label. As a result, the accuracy of the proposed method was up to 7.8% higher than that of the convolutional neural network (CNN). Furthermore, through the proposed class label reconstruction, we found a new mixed-type defect class that had not been found until now, and we detected new types of unknown defects that were not used for learning with an average accuracy of over 73%.