Integrating Local and Global Features for Wafer Defect Pattern Classification via Sequential Hybrid Architecture

Wafer map defect pattern classification supports quality monitoring in semiconductor manufacturing, but public benchmark datasets such as WM-811K exhibit extreme class imbalance, where majority classes can dominate standard metrics. This study aims to improve minority class performance while maintaining inference efficiency. Building on an iFormer-based hybrid backbone, we propose the Pattern-Selective Sequential Hybrid Network (PSS-HNet), which redesigns attention blocks to sequentially integrate local interaction (Modulated Convolution) and global interaction (Modulated Axial Attention) and applies sigmoid-based gating to control contextual information injection. Experiments on WM-811K (9 classes) compare iFormer (baseline), Axial-only, Axial+Modulation, and PSS-HNet using macro-averaged metrics as primary indicators, along with class-wise analysis and efficiency evaluation. PSS-HNet improves Macro-Recall by 1.02 percentage points (from 0.8852 to 0.8954) and Macro-F1 by 0.54 percentage points (from 0.9044 to 0.9098) over the baseline while maintaining similar accuracy. It also reduces computational cost and inference latency to 0.754 G FLOPs, 4.381 M parameters, and 7.682 ms, compared with 1.103 G FLOPs, 6.245 M parameters, and 8.666 ms for the baseline. Overall, selective sequential local–global integration provides a favorable balance between minority class performance and efficiency.