Wafer defect maps have different generation mechanisms according to the defect pattern, and automatic classification of wafer maps is therefore critical to reveal the root cause of the defects. In this paper, we examine the open set recognition problem, in which not only must wafer maps be classified using major defect patterns that are already known but also unknown defect patterns must also be detected. Our model is an ensemble model of a one-versus-one method that uses a convolutional neural network as the base classifier for wafer map classification. The proposed model calculates a weighted mean score for each defect pattern and determines the presence or absence of a pattern based on this score. The weight is calculated based on the proximity of data groups in the feature space and can be considered a support level at which a new wafer map belongs to a specific defect pattern. An untrained wafer map input into the model has a low support level and thus does not belong to any known defect pattern. An experiment was conducted using work-site failure bit count maps.

中文翻译：

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支持用于晶圆图缺陷的开放集识别的加权集成模型

晶圆缺陷图根据缺陷模式有不同的生成机制，因此晶圆图的自动分类对于揭示缺陷的根本原因至关重要。在本文中，我们研究了开放集识别问题，其中不仅必须使用已知的主要缺陷模式对晶圆图进行分类，而且还必须检测未知的缺陷模式。我们的模型是一对一方法的集成模型，该模型使用卷积神经网络作为晶圆图分类的基本分类器。所提出的模型计算每个缺陷模式的加权平均分数，并根据该分数确定模式是否存在。权重是基于特征空间中数据组的接近度计算的，可以认为是新晶圆图属于特定缺陷模式的支持水平。输入到模型中的未经训练的晶圆图具有较低的支持水平，因此不属于任何已知的缺陷模式。使用工作现场故障位计数图进行了实验。