YSCANet: A Lightweight Multi-scale Self-Calibrating Feature Fusion Network for Wafer Defect Detection

This study presents a lightweight multi-scale self-calibrating feature fusion network (YSCANet) to address critical challenges in wafer defect detection, including low recognition accuracy for morphologically similar defects, large inter-class scale variations, and severe category imbalance. The proposed framework integrates three key innovations: First, the self-calibrating feature fusion block (SCFFB) to enhance multiscale defect feature representation through cross-scale channel recalibration. Second, the Context Anchor Attention (CAA) mechanism optimizes spatial-semantic correlations via anchor point weighting, effectively resolving feature ambiguity in tiny defects. Third, dynamic sample reweighting via Focal Loss to mitigate class imbalance effects. Experimental validation on the WM-811K dataset demonstrates that YSCANet achieves a state-of-the-art average classification accuracy of 97.72\% (0.76\% higher than existing state-of-the-art methods), while reducing defect leakage rates by approximately 50\%. The network exhibits exceptional computational efficiency with an inference speed of 151.28 FPS and a compact parameter size of 2.07M. Comparative analysis shows that YSCANet has superior performance among similar defect detection solutions. These advances make YSCANet a powerful solution for real-time, high-precision wafer detection in semiconductor manufacturing, while balancing accuracy, efficiency and hardware deployment constraints.