A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing

Abstract

Fault detection is one of the most important research topics to guarantee safe operation and product quality consistency especially in the batch process of semiconductor manufacturing. However, the imbalanced fault data bring great challenges to extract the high nonlinearity and inherently time-varying dynamics of the batch process. Motivated by these, we propose a sequential oversampling discrimination approach for imbalanced batch process fault detection. Especially, different from the traditional oversampling methods, which extract temporal features from the whole process, we transform a whole batch sequence into multiple fixed-length sequences each batch by a sliding window, to extract the robust time-varying dynamics features. Then, an oversampling neural network is performed to balance both sequences of minority and majority classes. The needed sequences of the minority class are generated by an improved combination model of variational auto-encoder and generative adversarial network. Finally, a simplified sequential neural network is learned by the balanced-class sequences to perform the discrimination. We conduct extensive experiments based on two datasets of semiconductor manufacturing. One is a benchmark dataset and the other is a dataset from a real production line. The results achieved significant improvement, compared with other state-of-art fault detection methods and oversampling techniques.