用半监督对比学习提升晶圆图案识别准确率。
Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition
- 结合均值教师与监督对比损失，提升小样本下的识别能力。
- 在真实数据集上实现准确率提升5.46%，F1值提高4.53%。
- 适合半导体制造中标签稀缺场景的自动化缺陷检测。
晶圆图上的图案对工程师定位半导体制造中的生产问题至关重要。为降低成本并提升精度，自动化技术不可或缺，近年来深度学习在晶圆图模式识别中取得显著成果。受半监督学习和对比学习有效性的启发，本文提出将均值教师框架与监督对比损失相结合的新方法，以增强晶圆图模式识别能力。该方法不仅捕捉晶圆图案的细微特征，还应对标注数据有限的挑战。为缓解数据不平衡问题，采用SMOTE与欠采样技术优化数据分布。在来自半导体厂商的真实数据集WM811K上进行实验，结果表明，相比基线方法，本方法在准确率、精确率、召回率和F1分数上分别提升5.46%、6.68%、5.42%和4.53%。
原文摘要 · Abstract (English)
The patterns on wafer maps play a crucial role in helping engineers identify the causes of production issues during semiconductor manufacturing. In order to reduce costs and improve accuracy, automation technology is essential, and recent developments in deep learning have led to impressive results in wafer map pattern recognition. In this context, inspired by the effectiveness of semi-supervised learning and contrastive learning methods, we introduce an innovative approach that integrates the Mean Teacher framework with the supervised contrastive learning loss for enhanced wafer map pattern recognition. Our methodology not only addresses the nuances of wafer patterns but also tackles challenges arising from limited labeled data. To further refine the process, we address data imbalance in the wafer dataset by employing SMOTE and under-sampling techniques. We conduct a comprehensive analysis of our proposed method and demonstrate its effectiveness through experiments using real-world dataset WM811K obtained from semiconductor manufacturers. Compared to the baseline method, our method has achieved 5.46%, 6.68%, 5.42%, and 4.53% improvements in Accuracy, Precision, Recall, and F1 score, respectively.
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