Contrastive Learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition

Recognizing defect patterns in semiconductor wafer bin maps (WBMs) poses a critical challenge in the integrated circuit (IC) manufacturing industry. The accurate classification and segmentation of these defect patterns are of utmost significance as they are key to tracing the root causes of defects, thereby reducing costs and enhancing both product efficiency and quality. As the manufacturing process grows in complexity, the WBM becomes intricate when multiple defect patterns coexist on a single wafer, making the recognition task increasingly complicated. In addition, traditional supervised learning methods require a large number of labeled samples, which is labor-intensive. In this paper, we present a self-supervised contrastive learning framework for the classification and segmentation of mixed-type WBM defect patterns. Our model incorporates a global module for contrastive learning that captures image-level representations, alongside a local module that targets the comprehension of regional details, which is helpful for the segmentation of defective patterns. Experimental results demonstrate that our model performs effectively in scenarios where there is a limited number of labeled examples and a wealth of unlabeled ones.