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.

翻译：晶圆图上的模式在帮助工程师识别半导体制造过程中生产问题的原因方面起着至关重要的作用。为了降低成本并提高准确性，自动化技术至关重要，而深度学习的最新进展在晶圆图模式识别方面取得了令人瞩目的成果。在此背景下，受半监督学习和对比学习方法有效性的启发，我们提出了一种创新方法，该方法将均值教师框架与监督对比学习损失相结合，以增强晶圆图模式识别。我们的方法不仅考虑了晶圆模式的细微差别，还解决了因标记数据有限而带来的挑战。为了进一步完善流程，我们通过采用SMOTE和欠采样技术来解决晶圆数据集中的数据不平衡问题。我们对所提出的方法进行了全面分析，并通过使用从半导体制造商获得的真实数据集WM811K进行实验，证明了其有效性。与基线方法相比，我们的方法在准确率、精确率、召回率和F1分数上分别实现了5.46%、6.68%、5.42%和4.53%的提升。