Recently, machine learning has been effectively applied in the automation of wafer map pattern classification in semiconductor manufacturing. One conventional approach is to extract handcrafted features from a wafer map and build an off-the-shelf classifier on top of the features. Another approach is to use a convolutional neural network that operates directly on a wafer map. These two approaches have different strengths for different classes of wafer map defect patterns. In this study, we present a hybrid method that leverages the advantages of both approaches to improve the classification accuracy. First, we build two base classifiers using each of the approaches. Then, we build a stacking ensemble that combines the outputs of these base classifiers for the final prediction. The stacking ensemble classifies a wafer map by assigning a larger weight to the output of the superior base classifier with respect to each defect class. We demonstrate the effectiveness of the proposed method using real-world data from a semiconductor manufacturer.

最近，机器学习已有效地应用于半导体制造中晶圆图图案分类的自动化。一种常规方法是从晶片图提取手工制作的特征，并在特征之上构建现成的分类器。另一种方法是使用直接在晶圆图上运行的卷积神经网络。对于不同种类的晶圆图缺陷图案，这两种方法具有不同的优势。在这项研究中，我们提出了一种混合方法，该方法利用了两种方法的优点来提高分类精度。首先，我们使用每种方法构建两个基本分类器。然后，我们构建一个堆叠合奏，将这些基本分类器的输出组合起来以进行最终预测。通过针对每个缺陷类别为上级基础分类器的输出分配更大的权重，堆叠集成对晶圆图进行分类。我们使用来自半导体制造商的实际数据证明了该方法的有效性。