Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers

• URL: http://arxiv.org/abs/2504.02494v1
• Date: Thu, 03 Apr 2025 11:18:00 GMT
• Title: Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers
• Authors: Faisal Mohammad, Duksan Ryu,
• Abstract summary: We propose ViT-Tiny, a lightweight Vision Transformer (ViT) framework optimized for wafer defect classification.<n>ViT-Tiny outperforms its ViT-Base counterpart and state-of-the-art (SOTA) models, such as MSF-Trans and CNN-based architectures.<n>It achieves an F1-score of 98.4%, surpassing MSF-Trans by 2.94% in four-defect classification, improving recall by 2.86% in two-defect classification, and increasing precision by 3.13% in three-defect classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semiconductor wafer defect classification is critical for ensuring high precision and yield in manufacturing. Traditional CNN-based models often struggle with class imbalances and recognition of the multiple overlapping defect types in wafer maps. To address these challenges, we propose ViT-Tiny, a lightweight Vision Transformer (ViT) framework optimized for wafer defect classification. Trained on the WM-38k dataset. ViT-Tiny outperforms its ViT-Base counterpart and state-of-the-art (SOTA) models, such as MSF-Trans and CNN-based architectures. Through extensive ablation studies, we determine that a patch size of 16 provides optimal performance. ViT-Tiny achieves an F1-score of 98.4%, surpassing MSF-Trans by 2.94% in four-defect classification, improving recall by 2.86% in two-defect classification, and increasing precision by 3.13% in three-defect classification. Additionally, it demonstrates enhanced robustness under limited labeled data conditions, making it a computationally efficient and reliable solution for real-world semiconductor defect detection.

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