Semi-supervised imbalanced classification of wafer bin map defects using a Dual-Head CNN

Abstract

Wafer Bin Map (WBM) defect patterns are a critical aspect of identifying the root cause of manufacturing defects in the semiconductor industry. Semi-supervised learning (SSL) approaches have gained popularity for this purpose, as they can leverage both labeled and unlabeled data to improve model performance. However, SSL of WBM defect patterns is challenging due to class imbalance, where some defect classes have many more examples than others. Most of the existing SSL approaches assume a balanced dataset and often fail to provide satisfactory results when applied to imbalanced class problems. To address this issue, this work proposes a novel Dual-Head Convolutional Neural Network (CNN) architecture that contains two classifier heads. One classifier head maximizes overall classification scores, while the other aims to maximize per-class classification scores, providing equal attention to both majority and minority classes. The proposed CNN architecture uses pseudo-labels selected based on the outputs of these two classifiers to expand the labeled training set, which is then used to retrain the CNN. In this way, highly confident pseudo-labels are selected even from the minority classes, leading to better model training. Experiments show that the proposed approach is effective in handling class-imbalanced classification of WBM defect patterns, reporting state-of-the-art classification with an F1 score of 0.918, accuracy of 98.2% and a mean per-class accuracy of 91.7% using a lightweight ResNet-10 model as the backbone on the real-world public WBM dataset, WM-811K. The proposed approach’s success suggests that it could be a valuable tool for improving the accuracy and reliability of WBM defect pattern classification in semiconductor manufacturing. The code is available at https://github.com/M-Siyamalan/SSL-DHCNN.