  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.
