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.

中文翻译：

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离子磨刻蚀过程的故障预测的两阶段转移学习

在多种故障模式下的故障预测对于半导体制造中复杂工具的预测维护至关重要。但是，在实际的工业场景中，不同工具之间固有的数据差异和有限的故障数据导致数据不平衡并存，这使这项任务颇具挑战性。因此，本文提出了一种基于两阶段深度转移学习的新颖框架，用于在多种故障模式下进行预后，旨在准确预测离子磨机蚀刻过程的失效时间。在第一阶段，选择基本故障模式，并通过领域对抗学习对来自多个工具的状态监视数据进行数据对齐，其中嵌入时间卷积网络以从时间序列传感器数据中学习时间表示。第二阶段处理剩余故障模式的预后任务，第一阶段训练有素的深度模型被用作预训练模型，它将与其他故障模式中相对少量的数据进行微调，进一步加快训练过程并提高预测性能。在现实世界中的IME数据集上进行了全面的实验，结果表明，与其他现有方法相比，该模型不仅具有更好的预测精度，而且节省了很多训练时间。