Dynamic Clustering for Wafer Map Patterns Using Self-Supervised Learning on Convolutional Autoencoders

Abstract

Defect pattern analysis in wafer bin maps (WBM) plays a significant role in the semiconductor manufacturing process because it helps identify problematic steps or equipment so that process engineers can take appropriate actions to improve the overall yield. Clustering algorithms have been widely used to detect different defect patterns. However, most clustering algorithms, such as K-means clustering and self-organizing map, are required to determine the number of clusters in advance. To resolve this issue, we propose a self-supervised learning-based dynamic WBM clustering method. The proposed model first uses pseudo-labeled data, of which, the labels are dynamically determined by the Dirichlet process mixture model (DPMM). Thereafter, it is fine-tuned using pseudo-labels in a self-supervised manner. Experimental results based on the WM-811K dataset indicate that the proposed model not only outperforms the benchmark models but also demonstrates robustness to hyperparameters. In addition, the defect patterns identified by our model are more accurately and distinctively localized than those identified by the benchmark models.