Wafer maps contain information about various defect patterns on the wafer surface and automatic classification of these defects plays a vital role to find their root causes. Semiconductor engineers apply various methods for wafer defect classification such as manual visual inspection or machine learning-based algorithms by manually extracting useful features. However, these methods are unreliable, and their classification performance is also poor. Therefore, this paper proposes a deep learning-based convolutional neural network for automatic wafer defect identification (CNN-WDI). We applied a data augmentation technique to overcome the class-imbalance issue. The proposed model uses convolution layers to extract valuable features instead of manual feature extraction. Moreover, state-of-the-art regularization methods such as batch normalization and spatial dropout are used to improve the classification performance of the CNN-WDI model. The experimental results comparison using a real wafer dataset shows that our model outperformed all previously proposed machine learning-based wafer defect classification models. The average classification accuracy of the CNN-WDI model with nine different wafer map defects is 96.2%, which is an increment of 6.4% from the last highest average accuracy using the same dataset.

中文翻译：

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用于半导体制造过程中不平衡数据集的晶圆缺陷识别的深度卷积神经网络

晶圆图包含有关晶圆表面各种缺陷模式的信息，这些缺陷的自动分类对于找到它们的根本原因起着至关重要的作用。半导体工程师通过手动提取有用的特征，应用各种方法进行晶圆缺陷分类，例如手动视觉检查或基于机器学习的算法。但是，这些方法都不可靠，分类性能也很差。因此，本文提出了一种基于深度学习的卷积神经网络，用于自动晶圆缺陷识别（CNN-WDI）。我们应用了数据增强技术来克服类不平衡问题。所提出的模型使用卷积层来提取有价值的特征，而不是手动提取特征。而且，使用最先进的正则化方法（例如批量归一化和空间丢失）来提高 CNN-WDI 模型的分类性能。使用真实晶圆数据集的实验结果比较表明，我们的模型优于所有先前提出的基于机器学习的晶圆缺陷分类模型。具有九种不同晶圆图缺陷的 CNN-WDI 模型的平均分类精度为 96.2%，比使用相同数据集的最后一个最高平均精度增加了 6.4%。