用自编码器增强数据，提升晶圆缺陷分类准确率。
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
- 在隐空间加噪生成新数据，缓解类别不平衡
- 在WM-811K数据集上达98.56%准确率
- 适合需要高精度缺陷识别的半导体厂商
在半导体制造中，晶圆缺陷图（WDMs）对诊断问题和提升工艺良率至关重要，能揭示关键缺陷模式。然而，由于数据噪声、缺陷类别不平衡及失效模式复杂，准确分类仍具挑战。为此，本文提出一种结合自编码器数据增强与卷积神经网络（CNN）的新方法。通过在隐空间引入噪声，自编码器提升了数据多样性，缓解了类别不平衡，增强了模型泛化能力。使用增强后的数据训练CNN，可精准识别常见与罕见缺陷模式。在WM-811K数据集上的实验表明，该方法分类准确率达98.56%，较随机森林、SVM和逻辑回归分别提升19%、21%和27%。结果验证了该方法的鲁棒性与有效性，为晶圆缺陷检测与分类提供可靠解决方案。
原文摘要 · Abstract (English)
In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure modes. To address these challenges, this study proposes a novel method combining a self-encoder-based data augmentation technique with a convolutional neural network (CNN). By introducing noise into the latent space, the self-encoder enhances data diversity and mitigates class imbalance, thereby improving the model's generalization capabilities. The augmented dataset is subsequently used to train the CNN, enabling it to deliver precise classification of both common and rare defect patterns. Experimental results on the WM-811K dataset demonstrate that the proposed method achieves a classification accuracy of 98.56%, surpassing Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively. These findings highlight the robustness and effectiveness of the proposed approach, offering a reliable solution for wafer defect detection and classification.
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