用轻量版视觉变压器提升晶圆缺陷分类精度与效率
Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers
- 采用小尺寸视觉变压器，优化晶圆缺陷识别
- 在四类缺陷分类中F1达98.4%，优于现有模型2.94%
- 适合数据少、计算资源有限的工业检测场景
半导体晶圆缺陷分类对保障制造精度和良率至关重要。传统基于CNN的模型在处理缺陷类别不平衡及多重重叠缺陷类型时表现不佳。为此，我们提出ViT-Tiny，一种专为晶圆缺陷分类优化的轻量级视觉变换器框架，基于WM-38k数据集训练。实验表明，ViT-Tiny性能超越其基线模型ViT-Base及当前最优模型MSF-Trans与基于CNN的架构。通过大量消融实验，确定16×16的图像块大小为最佳配置。在四类缺陷分类中，F1分数达到98.4%，较MSF-Trans提升2.94%；在两类缺陷分类中召回率提高2.86%，三类分类中精确率提升3.13%。此外，在标注数据有限条件下仍表现出更强鲁棒性，是一种高效可靠的工业级晶圆缺陷检测方案。
原文摘要 · Abstract (English)
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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