Improved wafer map defect pattern classification using automatic data augmentation based lightweight encoder network in contrastive learning

In recent years, supervised learning has been the predominant method for wafer map defect pattern classification (WM-DPC), requiring a substantial amount of labeled data to build effective models. Nonetheless, gathering industrial data is challenging and demands significant manual labeling efforts, making it both expensive and time-consuming. To overcome these obstacles, we introduced a contrastive learning framework for WM-DPC based on automatic data augmentation. This innovative augmentation approach takes account of the regional defect density characteristic of various defect types, addressing the limitations of traditional fixed data augmentation and improving the model’s generalization capacity. The framework operates in two phases. At first, a lightweight encoder extracts rich representative features from unlabeled data. Then, the classification network is fine-tuned with a limited labeled data set. Experimental outcomes using the public WM-811K dataset showed that the proposed automatic data augmentation and lightweight encoder effectively captured detailed representative features from unlabeled data, and achieved an average accuracy close to 91% after fine-tuning with minimal labeled data.