Abstract
This paper presents a model of Elman recurrent neural network (ERNN) for time series fault prediction in semiconductor etch equipment. ERNN maintains a copy of previous state of the input in its context units, as well as the current state of the input. Derivative dynamic time warping (DDTW) method is also discussed for the synchronization of time series data set acquired from plasma etcher. For each parameter of the data, the best ERNN structure was selected and trained using Levenberg Marquardt to generate one-step-ahead prediction for 10 experimental runs. The faulty experimental runs were successfully distinguished from healthy experimental runs with one missed alarm out of ten experimental runs.
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References
Kerdprasop, K., Kerdprasop, N.: Feature Selection and Boosting Techniques to Improve Fault Detection Accuracy in the Semiconductor Manufacturing Process. In: Int. Multi. Conf. Engineers and Computer Scientists 2011, pp. 398–403. IAENG, Hong Kong (2011)
Stokes, D., May, G.S.: Real-Time Control of Reactive Ion Etching Using Neural Networks. IEEE Trans. Semi. Manufac. 13, 469–480 (2000)
Hong, S.J., May, G.S.: Neural Network Based Time Series Modeling of Optical Emission Spectroscopy Data for Fault Detection in Reactive Ion Etching. In: SPIE Conf. Adv. Microelectronic Manufacturing, vol. 5041, pp. 1–8 (2003)
Hong, S.J., May, G.S., Park, D.-C.: Neural Network Modeling of Reactive Ion Etching Using Optical Emission Spectroscopy Data. IEEE Trans. Semi. Manufac. 16, 598–608 (2003)
May, G.S.: Manufacturing ICs the Neural Way. IEEE Spectrum 31, 47–51 (1994)
Baker, M., Himmel, C., May, G.S.: Time Series Modeling of Reactive Ion Etching Using Neural Networks. IEEE Trans. Semi. Manufac. 8, 62–71 (1995)
Nadi, F., Agogino, A., Hodges, D.: Use of Influence Diagrams and Neural Networks in Modeling Semiconductor Manufacturing Processes. IEEE Trans. Semi. Manufac. 4, 52–58 (1991)
Hong, S.J., Lim, W.Y., Cheong, T.S., May, G.S.: Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing e-Diagnostics. IEEE Trans. Semi. Manufac. 25, 83–93 (2012)
Pearson, K.: Principal Components Analysis. The London, Edinburgh and Dublin Philosophical Magazine and Journal 6, 559 (1901)
Keogh, E.J., Pazzani, M.J.: Derivative dynamic time warping. In: First SIAM International Conference on Data Mining (SDM 2001), Chicago, USA (2001)
Bellman, R., Kalaba, R.: On adaptive control processes. IEEE Trans. Automat. Contr. 4, 1–9 (1959)
Senin, P.: Dynamic time warping algorithm review. University of Hawaii, Honolulu (2008)
Kassidas, A., MacGregor, J.F., Taylor, P.A.: Synchronization of batch trajectories using dynamic time warping. AIChE Journal 44, 864–875 (1998)
Elman, J.L.: Finding Structure in Time. Cognitive Science 14, 179–211 (1990)
Mankar, V.R., Ghatol, A.A.: Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal. Advances in Artificial Neural Systems (2009)
Swingler, K.: Applying Neural Networks, A Practical Guide. Morgan Kaufmann, San Francisco (1996)
Singh, E.: Comparative Economic Forecasting with Neural Networks Forecasting Aggregate Business Sales from S&P 500 and Interest Rates. Pace University, New York (2007)
Marquardt, D.W.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics 11, 431–441 (1963)
Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math 2, 164–168 (1944)
Dohnal, J.: Using of Levenberg-Marquardt method in identification by Neural Networks. In: Student EEICT 2004, pp. 361–365 (2004)
Hagan, M.T., Menhaj, M.B.: Training Feed forward Networks with the Marquardt Algorithm. IEEE Trans. on Neural Networks 5, 989–993 (1994)
Du, K.L., Swamy, M.N., Swamy, S.: Neural Networks in a soft computing Framework. Springer, London (2006)
Si, Nelson, Runger: Artificial Neural Network Models for Data Mining. In: The Handbook of Data Mining. Lawrence Erlbaum Associates Publishers, New Jersey (2003)
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Nawaz, J.M., Arshad, M.Z., Hong, S.J. (2013). Time Series Fault Prediction in Semiconductor Equipment Using Recurrent Neural Network. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_56
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DOI: https://doi.org/10.1007/978-3-642-39068-5_56
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