论文标题
基于拓扑数据分析的晶圆缺陷模式分类的新方法
A novel approach for wafer defect pattern classification based on topological data analysis
论文作者
论文摘要
在半导体制造中，晶圆图缺陷模式为设施维护和产量管理提供了关键信息，因此缺陷模式的分类是制造过程中最重要的任务之一。在本文中，我们提出了一种新颖的方式来表示缺陷模式作为有限维矢量的形状，该矢量将用作分类神经网络算法的输入。主要思想是使用拓扑数据分析（TDA）的持续同源性理论提取每种模式的拓扑特征。通过使用模拟数据集进行的一些实验，我们表明，与使用卷积神经网络（CNN）的方法相比，该方法在训练方面的训练速度更快，更有效，这是晶圆映射缺陷模式分类的最常见方法。此外，当培训数据的数量不够并且不平衡时，我们的方法优于基于CNN的方法。
In semiconductor manufacturing, wafer map defect pattern provides critical information for facility maintenance and yield management, so the classification of defect patterns is one of the most important tasks in the manufacturing process. In this paper, we propose a novel way to represent the shape of the defect pattern as a finite-dimensional vector, which will be used as an input for a neural network algorithm for classification. The main idea is to extract the topological features of each pattern by using the theory of persistent homology from topological data analysis (TDA). Through some experiments with a simulated dataset, we show that the proposed method is faster and much more efficient in training with higher accuracy, compared with the method using convolutional neural networks (CNN) which is the most common approach for wafer map defect pattern classification. Moreover, our method outperforms the CNN-based method when the number of training data is not enough and is imbalanced.
