SWaCo: Safe Wafer Bin Map Classification With Self-Supervised Contrastive Learning

Abstract

Defect patterns exhibited in wafer bin maps (WBMs) can provide essential clues about critical process failures to field engineers. In modern manufacturing processes, the automatic WBM defect pattern classification is critical for yield improvement. Although it is difficult to collect sufficient labels while a lot of unlabeled data is given, most existing studies have mainly used only labeled WBM data. Moreover, the unlabeled out-of-distribution (OOD) WBMs are inevitably collected. It degrades the performance of semi-supervised models. To this end, we propose a method for safe wafer bin map classification with self-supervised contrastive learning (SWaCo) to effectively exploit the unlabeled data with OOD. We propose a loss function to utilize label information in the pre-training step to learn more suitable representations for the downstream WBM classification task. The negative labeled examples of the same class as the anchor are used for additional positive examples. Moreover, we investigate proper data augmentation for WBM self-supervised contrastive learning. To evaluate the performance and the applicability of the proposed method, experiments are conducted on a public benchmark WBM dataset, WM-811K. The results demonstrate that the proposed method achieves better classification accuracy than existing methods, especially when only a small amount of class information is given.