Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing

• Published in: IEEE transactions on semiconductor manufacturing Vol. 33; no. 3; pp. 445 - 453
• Main Authors: Azamfar, Moslem, Li, Xiang, Lee, Jay
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Artificial neural networks; Condition monitoring; Convolutional neural networks; Data collection; Deep learning; domain adaptation; Domains; Fault diagnosis; Feature extraction; Industrial applications; Inspection; Machine learning; Manufacturing; Methods; Quality inspection; Semiconductor device manufacture; semiconductor manufacturing
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2020.2995548

Quality inspection in semiconductor manufacturing is of great importance in the modern industries. In the recent years, intelligent data-driven condition monitoring methods have been successfully developed and applied in the industrial applications. However, despite the promising condition monitoring performance, the existing methods generally assume the training and testing data are from the same distribution. In practice, due to variations in manufacturing process, the collected data are usually subject to different distributions in different operating conditions, that significantly deteriorates the performance of the data-driven methods. To address this issue, this paper proposes a deep learning-based domain adaptation method for fault diagnosis in semiconductor manufacturing. The maximum mean discrepancy metric is optimized on the learned high-level data representation in the deep neural network. Experimental results on a real-world semiconductor manufacturing dataset suggest the proposed method offers an effective and generalized data-driven fault diagnosis approach for quality inspection.