Decision fusion approach for detecting unknown wafer bin map patterns based on a deep multitask learning model

Abstract

In semiconductor manufacturing, wafer bin maps (WBMs) present specific defect patterns that provide crucial information for tracking abnormal processes. Thus, it is necessary to correctly classify WBM defects to achieve yield improvements. However, unknown types of defects constantly emerge due to the development of new process technologies and devices, degrading classification performance. Thus, a novel open set recognition (OSR) method that aims to detect unknown defects while correctly classifying known defect types is proposed in this work. The proposed method first extracts the reconstruction errors using an autoencoder and the random network errors using a random network distillation technique. Next, the two types of errors are combined by applying the extreme value theory-based Weibull calibration technique to produce probability scores for unknown detection. Finally, a deep convolutional neural network classifier assigns known classes to the samples determined as known. In the proposed method, multiple networks are interconnected, and we apply a simple sequential multitask learning mechanism to coordinate all networks. Extensive experiments were conducted on a real-world WBM defect dataset, and the results showed that each component proposed in this paper helped improve the unknown detection performance of our approach while minimizing its closed set classification performance loss. As a result, the proposed method outperformed the state-of-the-art OSR methods, achieving area under the receiver operating characteristic curve (AUROC) increases of at least 0.124 for unknown detection cases.