SemiWaferNet: Efficient Semi-Supervised Hybrid CNN–Transformer Models for Wafer Defect Classification and Segmentation

Wafer defect analysis is important for semiconductor manufacturing, but labeled data are limited, and class distributions are highly imbalanced. We present a semi-supervised framework with two lightweight hybrid CNN–Transformer models for wafer defect classification and segmentation. For classification, HybridCNN-ViT combines CNN-based local feature extraction with Transformer-based global context modeling, and adopts a three-stage progressive pseudo-labeling strategy to leverage unlabeled samples. The pseudo-label selection mechanism is systematically calibrated to improve pseudo-label reliability under limited labeled data. For segmentation, ConvoFormer-UNet integrates convolution-enhanced embeddings with Transformer blocks to balance boundary detail and global context. On the public WM-811K dataset, HybridCNN-ViT achieves 98.72% accuracy and 0.9985 macro-AUC under the semi-supervised setting for classification, while ConvoFormer-UNet reaches 99.19% IoU for segmentation with fewer parameters than several baselines. We also report efficiency on a single GPU to illustrate practical inference speed.