Evidential feature differentiated learning for trustworthy recognition of mixed-type defects in wafer maps

Wafer defect recognition is crucial for semiconductor manufacturing. During wafer fabrication, mixed-type defects with various morphologies, along with random noise, introduce data uncertainty into the wafer defect recognition process. However, most existing recognition methods fail to account for this uncertainty, leading to potentially unreliable results. In this paper, we propose a trustworthy mixed-type wafer defect recognition method (TMWDM) based on evidence theory, which models and leverages the evidential support of individual features during training to enhance recognition robustness. TMWDM consists of two main components: a directional evidential feature discrimination network and an evidential feature differentiated learning strategy. The former converts extracted features into evidence representations, enabling the evaluation of the directional alignment and strength of each feature’s evidential support under varying input and noise conditions. The latter employs an uncertainty-sensitive loss function that incorporates a penalty term measured using evidential features to optimise model learning under data uncertainty. Extensive experiments demonstrate that TMWDM consistently achieves over 98.80% in different recognition metrics across all mixed-type wafer defect scenarios, outperforming state-of-the-art methods. It also generalises well to WM-811 K and shows significant gains in ablation study.