A study on failure prediction in a plasma reactor

IEEE Transactions on Semiconductor Manufacturing

Volumn 11, Issue 4, 1998, Pages 670-680

  Rietman, Edward A ^a,c   Beachy, Milton ^b  

^a LUCENT TECHNOLOGIES   (United States)

^b NOKIA RESEARCH CENTER   (United States)

^c IEEE   (United States)

Author keywords

Failure prediction; Neural networks; Plasma reactors; Time series analysis

Indexed keywords

FAILURE ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; PLASMA APPLICATIONS; TIME SERIES ANALYSIS;

FAILURE PREDICTION; PLASMA REACTOR;

SEMICONDUCTOR DEVICE MANUFACTURE;

EID: 0032203442     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/66.728564     Document Type: Article

References (32)

1. P. Smyth, "Hidden Markov models and neural networks for fault detection in dynamic systems," in Neural Networks for Signal Processing, III, Proc. 1993 IEEE-SP Workshop, C. A. Kamm, G. M. Kuhn, B. Yoon, R. Chiellappa, and S. Y. Kung, Eds. New York: IEEE Press, 1993, pp. 582-591.
2. E. Hatzipantelis, A. Murray, and J. Penman, "Comparing hidden Markov models with artificial neural network architectures for condition monitoring applications," in Proc. Artificial Neural Networks, June 1995, pp. 369-374.
3. O. A. Basir and H. C. Shen, "Modeling and fusing uncertain multisensory data," J. Robot. Syst., vol. 13, no. 2, pp. 95-109, 1996.
4. A. Alessandri and T. Parisini, "Model-based fault diagnosis using nonlinear estimators: A neural network approach," in Amer. Control Conf., June 1997.
5. W. Hafez, T. Ross, and D. Gadd, "Machine learning for model-based diagnosis," in Amer. Control Conf., July 1997.
6. I. K. Konstantopoulos and P. J. Antsaklis, "Controllers with diagnostic capabilities. A neural network implementation," J. Intell. Robot. Syst., vol. 12, pp. 197-228, 1995.
7. B. Dubuisson, M. H. Masson, and C. Frelicot, "Some topics in using pattern recognition for system diagnosis," Elektronnoe Modelirovanie, vol. 17, no. 5, pp. 76-88, 1995.
8. R. Isermann, "Process fault detection based on modeling and estimation methods - A survey," Automatica, vol. 20, no. 4, pp. 387-404, 1984.
9. W. W. Wei, Time Series Analysis Univariant and Multivariant Methods Redwood City, CA: Addison-Wesley, 1990.
10. M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes, Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1993.
11. A. J. Duncan, Quality Control and Industrial Statistics, 4th ed. Home-wood, IL: Irwin, 1974.
12. D. C. Montgomery, Introduction to Statistical Quality Control, 2nd ed. New York: Wiley, 1991.
13. G. S. May and C. J. Spanos, "Automated malfunction diagnosis of semiconductor fabrication equipment: A plasma etch application," IEEE Trans. Ssmiconduct. Manufact., vol. 6, pp. 28-40, Feb. 1993.
14. M. D. Baker, C. D., Himmel, and G. S. May, "Time series modeling of reactive ion etching using neural networks," IEEE Trans. Semiconduct. Manufact., vol. 8, pp. 62-71, Feb. 1995.
15. B. Kim and G. S. May, "Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system," IEEE Trans. Comp., Packag., Manufact. technol. C, vol. 20, pp. 39-47, Jan. 1997.
16. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd ed. San Mateo, CA: Morgan Kaufmann, 1988.
17. A. S. Weigend and N. A. Gershenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past. Reading, MA: Addison-Wesley, 1994.
18. A. N. Shiryaev, Probability, 2nd ed., R. P. Boas, Ed. New York: Springer, 1996, translated.
19. D. T. Gillespie, Markov Processes an Introduction for Physical Scientists. New York: Academic, 1992.
20. W. J. Stewart, Introduction to the Numerical Solution of Markov Chains. Princeton, NJ: Princeton Univ. Press, 1994.
21. D. Hilbert, "Mathematical problems 13. Impossibility of the solution of the general equation of the 7th degree by means of functions of only two arguments," Bull. Amer. Math. Soc., vol. 8, pp. 461-462, 1902.
22. A. N. Kolmogorov, "On the representation of continuous functions of many variables by superpositions of continuous functions of one variable and addition," Dokl. Akad. Nauk., vol. 114, pp. 679-681, 1957.
23. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing, Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: MIT Press, 1986, pp. 318-362.
24. M. H. Hassoun, Fundamentals of Artificial Neural Networks. Cambridge, MA: MIT Press, 1995.
25. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
26. D. E. Rumelhart and D. Zipser, "Feature discovery by competitive learning," in Parallel Distributed Processing, Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: MIT Press, 1986, pp. 151-193.
27. T. Kohonen, Self-Organization and Associative Memory, 2nd ed. New York: Springer-Verlag, 1988.
28. C. Cortes, L. D. Jackel, S. A. Solla, V. N. Vapnik, and J. S. Denker, "Learning curves: Asymptotic values and rate of convergence," in NIPS 6, Neural Information Systems, J. D. Cowan, G. Tesauro, and J. Alspector, Eds. San Francisco, CA: Morgan Kaufmann, 1994, vol. 6, pp. 327-334.
29. C. Cortes, L. D. Jackel, and W. P. Chiang, "Limitson learning machine accuracy imposed by data quality," in NIPS 7, Neural Information Processing Systems, G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA: MIT Press, 1995, pp. 239-245.
30. M. B. Kennel and S. Isabelle, "Method to distinguish possible chaos from colored noise and to determine embedding parameters," Phys. Rev. A, vol. 45, no. 6, pp. 3111-3118, 1992.
31. J. Theiler, S. Eubank, A. Longtin, B. Galdrikian, and J. D. Farmer, "Testing for nonlinearity in time series: The method of surrogate data," Physica D, vol. 58, pp. 77-94, 1992.
32. Y. Bar-Yam, Dynamics of Complex Systems. Reading, MA: Addison-Wesley, 1997.