A generative-adversarial-network-based temporal raw trace data augmentation framework for fault detection in semiconductor manufacturing

In modern semiconductor manufacturing, where sophisticated process control mechanisms are standard, processing tools are equipped with sensors that generate vast amounts of raw trace data for process monitoring and fault detection. However, one of the major challenges data scientists face is the scarcity of sufficient raw trace data for defective wafers, creating an imbalance that complicates the training of machine learning models for effective fault detection. To address this issue, this paper proposes novel data augmentation structures and strategies utilizing Cycle Generative Adversarial Networks (CycleGANs) as an artificial intelligence application to synthesize temporal raw trace data for defective wafers. The effectiveness of these methods is demonstrated using a real-world dataset from the thin-film process in semiconductor fabrication. Several machine learning classification models—Gaussian Naive Bayes, Adaptive Boosting, eXtreme Gradient Boosting, and Light Gradient Boosting Machine—are employed to evaluate the performance of the augmented data. The paper identifies the optimal augmentation structure and strategy to enhance classification performance within the CycleGAN-based framework. For the thin-film processing dataset under study, the best classification performance achieves an accuracy rate of up to 99.30%, with a notably low false negative rate of 6.45%.