Mask-aware wafer defect classification using a DenseNet-based CNN with explicit geometry masking and Grad-CAM explainability.
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Updated
Jan 4, 2026 - Jupyter Notebook
Mask-aware wafer defect classification using a DenseNet-based CNN with explicit geometry masking and Grad-CAM explainability.
Enterprise-grade ML platform for semiconductor wafer defect classification using ResNet-50 U-Net architecture with active learning.
Research-driven wafer defect classification framework combining classical ML and CNN-based approaches, with experimental analysis of few-shot learning under severe class imbalance and limited data regimes.
SimCLR self-supervised pretraining on 638k unlabeled WM-811K wafer maps, plus multi-seed ensembling, extending wafer-defect-classifier.
ResNet-18 wafer defect classifier trained on WM-811K — focal loss, CBAM attention, TTA, and Grad-CAM++ explainability. Macro-F1 0.916 on the 9-class labeled subset.
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