When Wafer Failure Pattern Classification Meets Few-shot Learning and Self-Supervised Learning

Due to advances in semiconductor processing technologies, wafer failure pattern detection plays a key role in preventing yield loss excursion events for semiconductor manufacturing. In the recent semiconductor industry, visible surface defects are still mainly being inspected manually, which may result in inevitably erroneous classification. Many machine learning techniques-based pioneered arts in academia have been proposed to aid wafer failure pattern classification. However, few of these attach importance to unlabeled information and alleviate the data imbalanced issue. Based on these concerns, this paper designs an end-to-end wafer defect classifier that unites the few-shot learning and self-supervised learning algorithms. The aim of applying the few-shot learning paradigm is to learn representations that generalize well to the minority defect pattern classes where only a few wafer images are available, while the self-supervision information containing the intrinsic correlations of unlabeled wafer maps and their augmentations is expected to enhance the few-shot learner. The experimental results demonstrate the proposed framework has superior performance compared to cutting-edge wafer defect classification methods.