Two-Stage Transfer Learning for Fault Prognosis of Ion Mill Etching Process

Abstract

Fault prognosis under multiple fault modes is critical to predictive maintenance of complex tools in semiconductor manufacturing. However, the inherent data discrepancy among different tools and data imbalance with limited fault data coexist in real industrial scenario, making the task quite challenging. Therefore, this article proposes a novel two-stage deep transfer learning-based framework for prognosis under multiple fault modes, which aims at accurately predicting the time-to-failure of an Ion mill etching process. In the first stage, a base fault mode is selected and data alignment on condition monitoring data from multiple tools is performed via domain adversarial learning, wherein the temporal convolutional network is embedded to learn temporal representations from time-series sensor data. The second stage handles the prognostic tasks with remaining fault modes, the well-trained deep model from the first stage is employed as a pre-trained model, which will be fine-tuned with a relatively small amount of data from other fault modes, further accelerating the training process and enhancing the prediction performance. Comprehensive experiments are carried out on a real-world IME dataset, and the results show that the proposed model not only achieves better prediction accuracy but also saves much time for training compared with other existing methods.