Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach
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Published
Nov 16, 2020
Yuan Di
Xiaodong Jia
Jay Lee
Abstract
As an essential process in semiconductor manufacturing, Chemical Mechanical Planarization has been studied in recent decades and the material removal rate has been proved to be a critical performance indicator. Comparing with after-process metrology, virtual metrology shows advantages in production time saving and quick response to the process control. This paper presents an enhanced material removal rate prediction algorithm based on an integrated model and data-driven method. The proposed approach combines the physical mechanism and the influence of nearest neighbors, and extracts relevant features. The features are then input to construct multiple regression models, which are integrated to obtain the final prognosis. This method was evaluated by the PHM 2016 Data Challenge data sets and the result obtained the best mean squared error score among competitors.
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Keywords
Virtual Metrology, Machine Learning, Chemical Mechanical Planarization
References
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Jia, X., Jin, C., Buzza, M., Wang, W., & Lee, J. (2016). Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves. Renewable Energy, 99, 1191-1201.
Kang, P., Kim, D., & Cho, S. (2016). Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Expert Systems with Applications, 51, 85-106.
Kang, P., Lee, H. J., Cho, S., Kim, D., Park, J., Park, C. K., & Doh, S. (2009). A virtual metrology system for semiconductor manufacturing. Expert Systems with Applications, 36(10), 12554-12561.
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Tso, P. L., & Ho, S. Y. (2007). Factors influencing the dressing rate of chemical mechanical polishing pad conditioning. The International Journal of Advanced Manufacturing Technology, 33(7-8), 720-724.
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Yeh, H. M., & Chen, K. S. (2010). Development of a pad conditioning simulation module with a diamond dresser for CMP applications. The International Journal of Advanced Manufacturing Technology, 50(1-4), 1-12.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica chimica acta, 185, 1-17.
Hirai, T., Hazama, K., & Kano, M. (2014, October). Application of locally weighted partial least squares to design of semiconductor virtual metrology. In 2014 IEEE Conference on Control Applications (CCA) (pp. 1771-1776). IEEE.
Hirai, T., & Kano, M. (2015). Adaptive virtual metrology design for semiconductor dry etching process through locally weighted partial least squares. IEEE Transactions on Semiconductor Manufacturing, 28(2), 137-144.
Hocheng, H., Tsai, H. Y., & Chen, L. J. (1997). A kinematic analysis of CMP based on velocity model. Proc. 2nd CMP-VMIC, 277-280.
Jebri, M. A., Graton, G., El Adel, E. M., Ouladsine, M., & Pinaton, J. (2016, May). Virtual metrology on Chemical Mechanical Planarization process based on Just-In-Time Learning. In 2016 5th International Conference on Systems and Control (ICSC) (pp. 169-174). IEEE.
Jia, X., Jin, C., Buzza, M., Wang, W., & Lee, J. (2016). Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves. Renewable Energy, 99, 1191-1201.
Kang, P., Kim, D., & Cho, S. (2016). Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Expert Systems with Applications, 51, 85-106.
Kang, P., Lee, H. J., Cho, S., Kim, D., Park, J., Park, C. K., & Doh, S. (2009). A virtual metrology system for semiconductor manufacturing. Expert Systems with Applications, 36(10), 12554-12561.
Kramer, O. (2011). Dimensionality reduction by unsupervised k-nearest neighbor regression. Paper presented at the Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on.
Lenz, B., & Barak, B. (2013, January). Data mining and support vector regression machine learning in semiconductor manufacturing to improve virtual metrology. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 3447-3456). IEEE.
Luo, J., & Dornfeld, D. A. (2001). Material removal mechanism in chemical mechanical polishing: theory and modeling. IEEE transactions on semiconductor manufacturing, 14(2), 112-133.
Luo, J., & Dornfeld, D. A. (2003). Review of chemical-mechanical planarization modeling for integrated circuit fabrication: From particle scale to die and wafer scales. Laboratory for Manufacturing and Sustainability.
Moyne, J., Del Castillo, E., & Hurwitz, A. M. (Eds.). (2001). Run-to-run control in semiconductor manufacturing (Vol. 200). Chichester: CRC press.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective: MIT press.
Park, C., & Kim, S. B. (2016). Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm. Journal of Process Control, 42, 51-58.
Parks, K., Wan, Y.-H., Wiener, G., & Liu, Y. (2011). Wind energy forecasting: a collaboration of the national center for atmospheric research (NCAR) and xcel energy. Contract, 303, 275-3000.
PHM Society 2016 Data Challenge Competition, (2016). [http://www.phmsociety.org/events/conference/phm/16/data-challenge]
Pillai, P., Kaushik, A., Bhavikatti, S., Roy, A., & Kumar, V. (2016). A Hybrid Approach for Fusing Physics and Data for Failure Prediction. International Journal of Prognostics and Health Management, 7(025), 1-12
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
Steigerwald, J. M., Murarka, S. P., & Gutmann, R. J. (2008). Chemical mechanical planarization of microelectronic materials. John Wiley & Sons.
Stine, B. E., Ouma, D. O., Divecha, R. R., Boning, D. S., Chung, J. E., Hetherington, D. L., ... & Oh, S. Y. (1998). Rapid characterization and modeling of pattern-dependent variation in chemical-mechanical polishing. IEEE Transactions on Semiconductor manufacturing, 11(1), 129-140.
Su, Y. C., Lin, T. H., Cheng, F. T., & Wu, W. M. (2008). Accuracy and real-time considerations for implementing various virtual metrology algorithms. IEEE Transactions on Semiconductor Manufacturing, 21(3), 426-434.
Tso, P. L., & Ho, S. Y. (2007). Factors influencing the dressing rate of chemical mechanical polishing pad conditioning. The International Journal of Advanced Manufacturing Technology, 33(7-8), 720-724.
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (1993). Probability and statistics for engineers and scientists (Vol. 5): Macmillan New York.
Wang, P., Gao, R. X., & Yan, R. (2017). A deep learning-based approach to material removal rate prediction in polishing. CIRP Annals-Manufacturing Technology.
Yeh, H. M., & Chen, K. S. (2010). Development of a pad conditioning simulation module with a diamond dresser for CMP applications. The International Journal of Advanced Manufacturing Technology, 50(1-4), 1-12.
Section
Technical Papers