Modeling with Thin Film Thickness using Machine Learning

  • Kim, Dong Hwan (Department of Electronics Engineering, Myongji University) ;
  • Choi, Jeong Eun (Department of Electronics Engineering, Myongji University) ;
  • Ha, Tae Min (Department of Electronics Engineering, Myongji University) ;
  • Hong, Sang Jeen (Department of Electronics Engineering, Myongji University)
  • 투고 : 2019.06.08
  • 심사 : 2019.06.24
  • 발행 : 2019.06.30

초록

Virtual metrology, which is one of APC techniques, is a method to predict characteristics of manufactured films using machine learning with saving time and resources. As the photoresist is no longer a mask material for use in high aspect ratios as the CD is reduced, hard mask is introduced to solve such problems. Among many types of hard mask materials, amorphous carbon layer(ACL) is widely investigated due to its advantages of high etch selectivity than conventional photoresist, high optical transmittance, easy deposition process, and removability by oxygen plasma. In this study, VM using different machine learning algorithms is applied to predict the thickness of ACL and trained models are evaluated which model shows best prediction performance. ACL specimens are deposited by plasma enhanced chemical vapor deposition(PECVD) with four different process parameters(Pressure, RF power, $C_3H_6$ gas flow, $N_2$ gas flow). Gradient boosting regression(GBR) algorithm, random forest regression(RFR) algorithm, and neural network(NN) are selected for modeling. The model using gradient boosting algorithm shows most proper performance with higher R-squared value. A model for predicting the thickness of the ACL film within the abovementioned conditions has been successfully constructed.

키워드

참고문헌

  1. Chen, W.C., Lee, A.H.I., Deng, W.J., and Liu, K.Y., "The implementation of neural network for semiconductor PECVD process," Expert Systems with Applications, Vol. 32, No. 4, pp. 1148-1153, 2007. https://doi.org/10.1016/j.eswa.2006.02.013
  2. Terzi, M., Masiero, C., Beghi, A., Maggipinto, M., and Susto, G.A., "Deep learning for virtual metrology: Modeling with optical emission spectroscopy data," 2017 Proc. IEEE 3rd Int. Forum Res. Technol. Soc. Ind., pp. 1-6.
  3. Kim, H.-C., and Seol, Y.T., "Development of Virtual Integrated Prototyping Simulation Environment for Plasma Chamber Analysis and Design(VIP-SEPCAD)," Journal of the Korean Society of Semiconductor Equipment Technology, Vol.2, No. 4, pp. 9-12, 2003.
  4. Hong, S., May, G.S., and Park, D.-C., "Neural network modeling of reactive ion etching using optical emission spectroscopy data," IEEE Trans. Semi. Manufac., Vol. 16, No. 4, pp. 598-608, 2003. https://doi.org/10.1109/TSM.2003.818976
  5. Kim, B., Park, K., and Lee, D. "Use of neural network to model the deposition rate of PECVD-silicon nitride films," Plasma Sources Science and Technology, Vol. 14, No. 1, pp. 83-88, 2005. https://doi.org/10.1088/0963-0252/14/1/011
  6. Purwins, H., Barak, B., Nagi, a., Engel, R., Hockele, U., Kyek. A., Cheria, S., Lenz, B., Pfeifer, G., and Weinziel, K., "Regression methods for virtual metrology of layer thickness in chemical vapor deposition," IEEE/ASME Trans. Mechatronics, Vol. 19, No. 1, pp. 1-8, 2013.
  7. Kang, P., Lee, H., Cho, S., Kim, D., Park, J., Park, C., and Doh, S., "A virtual metrology system for semiconductor manufacturing," Expert Systems with Applications Vol. 36, No. 10, pp. 12554-12561, 2009. https://doi.org/10.1016/j.eswa.2009.05.053
  8. Kim, J.K., Cho, S.I., Kim, N.G., Jhon, M.S., Min, K.S., Kim, C.K., and Yeom, G.Y., "Study on the etching characteristics of amorphous carbon layer in oxygen plasma with carbonyl sulfide," Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films Vol. 31, No. 2, p. 021301, 2013. https://doi.org/10.1116/1.4780122
  9. Hung, M.-H., Lin, T.-H, Cheng, F.-T., and Lin, R.-C., "A novel virtual metrology scheme for predicting CVD thickness in semiconductor manufacturing," IEEE/ASME Transactions on Mechatronics, Vol. 12, No. 3, pp. 308-316, 2007. https://doi.org/10.1109/TMECH.2007.897275
  10. Rietman, E., and Lory, E.R., "Use of neural networks in modeling semiconductor manufacturing processes: An example for plasma etch modeling," IEEE Trans. Semi Manufac., Vol. 6, No. 4, pp. 343-347, 1993. https://doi.org/10.1109/66.267644
  11. Yip, W.K., Law, K.G., and Lee, W.J., "Forecasting Final/Class Yield Based on Fabrication Process E-Test and Sort Data," 2007 IEEE Int. Conf. Automation Science and Engineering, Scottsdale, AZ, pp. 478-483.
  12. Cho, I.-H., Lee, N.-H., Chang, S.-W., An, S.-W., Yonn, Y.-H., Zoh, and K.-D., "Analysis of Characteristics and Optimization of Photo-degradation condition of Reactive Orange 16 Using a Box-Behnken Method," Journal of Korean Society of Environmental Engineers, Vol. 28, No. 9, pp. 917-925, 2006.
  13. Jayalakshmi, T., and Santhakumaran, A., "Statistical normalization and back propagation for classification, " International Journal of Computer Theory and Engineering, Vol. 3, No. 1, pp. 1793-8201, 2011.