Deep Learning Based Virtual Metrology and Yield Prediction in Semiconductor Manufacturing Processes

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Published Jul 14, 2017
DOI https://doi.org/10.36001/phmap.2017.v1i1.1691
Myong Kee Jeong
Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
Jeongsub Choi
Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
Youngdoo Son
Industrial and Systems Engineering, Dongguk University, Seoul, 04620, South Korea
Jihoon Kang
Algorithm Research team, Samsung SDS, Seoul, 06765, South Korea

Abstract

We present a deep learning based supervised autoencoder to extract meaningful features from massive in-line sensor functional signals of semiconductor manufacturing processes. Based on those extract features, we build the
virtual metrology model to predict important quality characteristics of the process and the yield prediction model. A real-life case is studied in this work, and the empirical results show that the proposed model outperforms general
approaches for the predictions using signal data.

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Keywords

PHM

References
Lee, H., Kim, Y., & Kim, C. O. (2017). A Deep Learning Model for Robust Wafer Fault Monitoring with Sensor Measurement Noise. IEEE Transactions on Semiconductor Manufacturing, vol. 30(1), pp. 23-31.
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, vol. 11, pp. 3371-3408.
Issue
Vol. 1 No. 1 (2017): Proceedings of the Asia Pacific Conference of the PHM Society 2017
Section
Invited Papers