Transformer模型在小样本晶圆缺陷检测中表现优于传统CNN，准确率达90.83%。
A Comparative Analysis of Semiconductor Wafer Map Defect Detection with Image Transformer
- 采用DeiT图像变压器模型处理数据稀缺的晶圆图缺陷分类问题。
- 在有限数据下实现90.83%准确率，显著高于VGG-19等传统CNN模型。
- 适合工业界开展晶圆制造预测性维护，尤其对少数缺陷类检测更鲁棒。
预测性维护是现代工业的重要领域，可提升故障检测效率并降低成本。通过在全流程中应用机器学习算法，缺陷检测可实现自动化。半导体行业对维护预测性要求高，但受限于数据量少且分布不均，传统卷积神经网络（CNN）如VGG-19、Xception和SqueezeNet虽在图像分类中表现良好，其性能常会下降。本研究探讨了数据高效图像变压器（DeiT）在数据受限条件下对晶圆图缺陷分类的应用效果。实验表明，DeiT模型达到最高分类准确率90.83%，优于VGG-19（65%）、SqueezeNet（82%）、Xception（66%）及混合模型（67%）。同时，DeiT在F1分数上达90.78%，训练收敛更快，对少数缺陷类别检测更具鲁棒性。结果表明，基于Transformer的模型如DeiT在半导体晶圆缺陷检测中具有巨大潜力，可支持半导体制造中的预测性维护策略。
原文摘要 · Abstract (English)
Predictive maintenance is an important sector in modern industries which improves fault detection and cost reduction processes. By using machine learning algorithms in the whole process, the defects detection process can be implemented smoothly. Semiconductor is a sensitive maintenance field that requires predictability in work. While convolutional neural networks (CNNs) such as VGG-19, Xception and Squeeze-Net have demonstrated solid performance in image classification for semiconductor wafer industry, their effectiveness often declines in scenarios with limited and imbalanced data. This study investigates the use of the Data-Efficient Image Transformer (DeiT) for classifying wafer map defects under data-constrained conditions. Experimental results reveal that the DeiT model achieves highest classification accuracy of 90.83%, outperforming CNN models such as VGG-19(65%), SqueezeNet(82%), Xception(66%) and Hybrid(67%). DeiT also demonstrated superior F1-score (90.78%) and faster training convergence, with enhanced robustness in detecting minority defect classes. These findings highlight the potential of transformer-based models like DeiT in semiconductor wafer defect detection and support predictive maintenance strategies within semiconductor fabrication processes.
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