A predictive maintenance system for epitaxy processes based on filtering and prediction techniques

IEEE Transactions on Semiconductor Manufacturing

Volumn 25, Issue 4, 2012, Pages 638-649

  Susto, Gian Antonio ^a   Beghi, Alessandro ^a   De Luca, Cristina ^b  

^a UNIVERSITY OF PADOVA   (Italy)

^b INFINEON TECHNOLOGIES AUSTRIA AG   (Austria)

Author keywords

Gaussian kernel density estimation; Kalman predictor; Particle filters; Predictive maintenance (PdM)

Indexed keywords

EPITAXIAL DEPOSITION; GAUSSIAN KERNELS; INDUSTRIAL PRODUCTION; KALMAN PREDICTOR; PARTICLE FILTER; PREDICTION TECHNIQUES; PREDICTIVE MAINTENANCE; SCHEDULING CONTROL;

BEHAVIORAL RESEARCH; DISTRIBUTED COMPUTER SYSTEMS; EPITAXIAL GROWTH; FORECASTING; MAINTENANCE; MONTE CARLO METHODS; SENSORS; SIGNAL FILTERING AND PREDICTION; TARGET TRACKING; TEMPERATURE MEASUREMENT;

SILICON WAFERS;

EID: 84877023031     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSM.2012.2209131     Document Type: Article

References (36)

1. G. A. Susto, A. Beghi, and C. De Luca, "A predictive maintenance system for silicon epitaxial deposition," in Proc. 7th IEEE Int. Conf. Autom. Sci. Eng., 2011, pp. 262-267.
2. A. Douchet, N. deFreitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. Berlin, Germany: Springer-Verlag, 2001.
3. J. Wong, T. Adamson, G. Moore, M. Passow, and V. Xu, "Strategies for successfully implementing fab-wide FDC methodologies in semiconductor manufacturing," in Proc. 17th AEC/APC Symp., 2006, pp. 101-102.
4. R. Mobley, An Introduction to Predictive Maintenance. Oxford, U.K.: Butterworth-Heinemann, 2002.
5. J. Hyde, J. Doxsey, and J. Card, "The use of unified APC/FD in the control of a metal etch area," in Proc. IEEE/SEMI Advanced Semicond. Manuf. Conf., May 2004, pp. 237-240.
6. D. Montgomery, Introduction to Statistical Quality Control. Delhi, India: Wiley-India, 2007.
7. A. Schirru, S. Pampuri, and G. DeNicolao, "Multilevel statistical process control of asynchronous multistream processes in semiconductor manufacturing," in Proc. IEEE Conf. Autom. Sci. Eng., Aug. 2010, pp. 57-62.
8. Q. He and J. Wang, "Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes," IEEE Trans. Semicond. Manuf., vol. 20, no. 4, pp. 345-350, Nov. 2007.
9. Q. He and J. Wang, "Large-scale semiconductor process fault detection using a fast pattern recognition-based method," IEEE Trans. Semicond. Manuf., vol. 23, no. 2, pp. 194-200, May 2010.
10. E. Rying, "A novel focused local learning wavelet network with application to in situ monitoring during selective silicon epitaxy," Ph.D. dissertation, Dept. Elect. Comput. Eng., North Carolina State Univ., Raleigh, 2001.
11. S. Pampuri, A. Schirru, C. DeLuca, and G. DeNicolao, "Proportional hazard model with l1 penalization applied to predictive maintenance in semiconductor manufacturing," in Proc. IEEE Conf. Autom. Sci. Eng., Aug. 2011, pp. 250-255.
12. B. Goodlin, D. Boning, H. Sawin, and B. Wise, "Simultaneous fault detection and classification for semiconductor manufacturing tools," J. Electrochem. Soc., vol. 150, no. 12, pp. 778-784, 2003.
13. J.-H. Pan and D. Tai, "A new strategy for defect inspection by the virtual inspection in semiconductor wafer fabrication," Comput. Ind. Eng., vol. 60, no. 1, pp. 16-24, 2011.
14. A. Chen and J. Blue, "Recipe-independent indicator for tool health diagnosis and predictive maintenance," IEEE Trans. Semicond. Manuf., vol. 22, no. 4, pp. 522-535, Nov. 2009.
15. K.-Y. Cheng, "Molecular beam epitaxy technology of III-V compound semiconductors for optoelectronic applications," Proc. IEEE, vol. 85, no. 11, pp. 1694-1714, Nov. 1997.
16. R. Kalman, "A new approach to linear filtering and prediction problems," J. Basic Eng., vol. 82, no. 1, pp. 35-45, 1960.
17. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
18. A. Schirru, S. Pampuri, and G. DeNicolao, "Particle filtering of hidden gamma processes for robust predictive maintenance in semiconductor manufacturing," in Proc. IEEE Conf. Autom. Sci. Eng., Aug. 2010, pp. 51-56.
19. S. Butler and J. Ringwood, "Particle filters for remaining useful life estimation of abatement equipment used in semiconductor manufacturing," in Proc. IEEE Conf. Control Fault-Tolerant Syst., Oct. 2010, pp. 436-441.
20. R. Jaeger, Introduction to Microelectronic Fabrication. Englewood Cliffs, NJ: Prentice-Hall, 2001.
21. M. Basseville and I. Nikiforov, Detection of Abrupt Changes: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1993.
22. D. Barry and J. A. Hartigan, "A Bayesian analysis for change point problems," J. Amer. Statist. Assoc., vol. 88, no. 421, pp. 309-319, 1993.
23. A. Chen and R.-S. Guo, "Age-based double EWMA controller and its application to CMP processes," IEEE Trans. Semicond. Manuf., vol. 14, no. 1, pp. 11-19, Feb. 2001.
24. R. Ramjee, B. Ray, and N. Crato, "A note on moving average forecasts of long memory processes with an application to quality control," Int. J. Forecasting, vol. 18, no. 2, pp. 291-297, Apr. 2002.
25. D. Cox, "Prediction by exponentially weighted moving averages and related methods," J. Roy. Statist. Soc. B Methodol., vol. 23, no. 2, pp. 414-422, 1961.
26. G. Susto, S. Pampuri, A. Schirru, and A. Beghi, "Optimal tuning of epitaxy pyrometers," in Proc. 23rd IEEE/SEMI Advanced Semicond. Manuf. Conf., May 2012, pp. 294-299.
27. W. Larimore, "Canonical variate analysis in identification, filtering and adaptive control," in Proc. 29th IEEE Conf. Decision Control, Dec. 1990, pp. 596-604.
28. A. Chiuso and G. Picci, "Prediction error versus subspace methods in closed-loop identification," in Proc. 16th IFAC World Congr., 2005.
29. A. Chiuso, "On the relation between CCA and predictor-based subspace identification," IEEE Trans. Automatic Control, vol. 52, no. 10, pp. 1795-1812, Oct. 2007.
30. P. Van Overschee and B. D. Moor, Subspace Identification for Linear Systems: Theory-Implementation-Applications. Dordrecht, The Netherlands: Kluwer Academic, 1996.
31. M. Jones, J. Marron, and S. Sheater, "Progress in data-based bandwidth selection for kernel density estimation," Comput. Statist., vol. 11, no. 3, pp. 337-381, 1996.
32. H. Lilliefors, "On the Kolmogorov-Smirnov test for normality with mean and variance unknown," J. Amer. Statist. Assoc., vol. 62, pp. 399-402, Jun. 1967.
33. S. Sheater and M. Jones, "A reliable data-based bandwidth selection method for kernel density estimation," J. Roy. Statist. Soc. Series B Methodol., vol. 53, no. 3, pp. 683-690, 1991.
34. Z. Botev, J. Grotowski, and D. Kroese, "Kernel density estimation via diffusion," Ann. Statist., vol. 38, no. 5, pp. 2916-2957, 2010.
35. G. Susto, A. Schirru, S. Pampuri, and A. Beghi, "A predictive maintenance system based on regularization methods for ion-implantation," in Proc. 23rd IEEE/SEMI Advanced Semicond. Manuf. Conf., May 2012, pp. 175-180.
36. Z. Botev. (2012). Kernel Density Estimation Using MATLAB, MATLAB Central File Exchange[Online]. Available: www.mathworks.us/ matlabcentral/ fileexchange/14034