Tool condition diagnosis with a recipe-independent hierarchical monitoring scheme

IEEE Transactions on Semiconductor Manufacturing

Volumn 26, Issue 1, 2013, Pages 82-91

  Blue, Jakey ^a   Gleispach, Dietmar ^b   Roussy, Agnès ^a   Scheibelhofer, Peter ^b  

^a ECOLE DES MINES DE SAINT ETIENNE   (France)

^b AMS AG   (Austria)

Author keywords

Fault detection and classification (FDC); Hotelling's T2; moving variance and covariance; tool condition diagnosis

Indexed keywords

EXTERNAL SENSORS; FAULT DETECTION AND CLASSIFICATION; FAULT DETECTION AND IDENTIFICATION; FAULT IDENTIFICATIONS; MOVING VARIANCE AND COVARIANCE; SEMICONDUCTOR MANUFACTURING; TOOL CONDITION DIAGNOSIS;

SEMICONDUCTOR DEVICE MANUFACTURE;

SENSORS;

EID: 84873372405     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSM.2012.2230279     Document Type: Article

References (25)

1. Annual Report: Brooks Automation 10-K. (2012, Nov.) [Online]. Available: http://www.wikinvest.com/stock/Brooks?Automation?(BRKS)/Filing/10-K/2007/ F2163986
2. T. He, W. Xie, Q. Wu, and T. Shi, "Process fault detection and diagnosis based on principal component analysis," in Proc. 15th Int. Conf. Mach. Learning Cybern., 2006, pp. 3551-3556. (Pubitemid 46430948)
3. M. A. G. Machado and A. F. B. Costa, "The use of principal components and univariate charts to control multivariate processes," Pesquisa Operacional, vol. 28, no. 1, pp. 173-196, 2008.
4. T. Biton and H. Ratner, "Multivariate fault detection (MVFD) EP/FDC implementation," in Proc. IEEE Int. Symp. Semicond. Manuf., Sep. 2005, pp. 377-380. (Pubitemid 41756249)
5. H. Wang, "A survey of maintenance policies of deteriorating systems," Eur. J. Oper. Res., vol. 139, no. 3, pp. 469-489, 2002. (Pubitemid 34249626)
6. G. A. Susto, A. Beghi, and C. De Luca, "A predictive maintenance system for silicon epitaxial deposition," in Proc. 6th IEEE Conf. Autom. Sci. Eng., Aug. 2011, pp. 262-267.
7. S. Pampuri, A. Schirru, C. De Luca, and G. De Nicolao, "Proportional hazard model with l1 penalization applied to predictive maintenance in semiconductor manufacturing," in Proc. 6th IEEE Conf. Autom. Sci. Eng., Aug. 2011, pp. 250-255.
8. A. Schirru, S. Pampuri, and G. De Nicolao, "Multilevel statistical process control of asynchronous multi-stream processes in semiconductor manufacturing," in Proc. 6th IEEE Conf. Autom. Sci. Eng., Toronto, Canada, Aug. 2010, pp. 57-62.
9. R. E. Barlow and F. Proschan, Mathematical Theory of Reliability. New York: Wiley, 1965.
10. J. J. McCall, "Maintenance policies for stochastically failing equipment: A survey," Manage. Sci., vol. 11, no. 5, pp. 493-524, 1965.
11. Y. S. Sherif and M. L. Smith, "Optimal maintenance models for systems subject to failure: A review," Naval Res. Logistics Quart., vol. 28, no. 1, pp. 47-74, 1981. (Pubitemid 11489880)
12. C. Valdez-Flores and R. M. Feldman, "A survey of preventive maintenance models for stochastically deteriorating single-unit systems," Naval Res. Logistics, vol. 36, no. 4, pp. 419-446, 1989. (Pubitemid 20625232)
13. R. Dekker, "Applications of maintenance optimization models: A review and analysis," Reliab. Eng. Syst. Safety, vol. 51, no. 3, pp. 229-240, 1996. (Pubitemid 126349477)
14. E. P. C. Kao, "Optimal replacement rules when changes of state are semi-Markovian," Oper. Res., vol. 21, no. 6, pp. 1231-1249, 1973.
15. A. Z. Milioni and S. R. Pliska, "Optimal inspection under semi-Markovian deterioration: Basic results," Naval Res. Logistics, vol. 35, no. 5, pp. 373-392, Oct. 1988.
16. R. H. Yeh, "Optimal inspection and replacement policies for multi-state deteriorating systems," Eur. J. Oper. Res., vol. 96, no. 2, pp. 248-259, 1996.
17. A. Chen and J. Blue, "Recipe-independent health indicator for tool predictive maintenance and fault diagnosis," IEEE Trans. Semicond. Manuf., vol. 22, no. 4, pp. 522-535, Nov. 2009.
18. R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ: Prentice-Hall, 2002, ch. 3, pp. 124-139.
19. J. P. Stevens, Applied Multivariate Statistics for the Social Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, 2001, ch. 2, pp. 64-70.
20. L. Mirsky, An Introduction to Linear Algebra. New York: Courier Dover, 1990, ch. 1, pp. 1-38.
21. T. W. Anderson, An Introduction to Multivariate Statistical Analysis. New York: Wiley, 1984, ch. 7, pp. 251-290.
22. F. Aparisi, J. Jabaloyes, and A. Carrión, "Statistical properties of the |S|multivariate control chart," Commun. Statist. Theory Methods, vol. 28, no. 11, pp. 2671-2686, 1999.
23. A. B. Yeh, D. K. J. Lin, and R. N. McGrath, "Multivariate control charts for monitoring covariance matrix: A review," J. Quality Technol. Quantitative Manage., vol. 3, no. 4, pp. 415-436, 2006.
24. J. M. Lucas and M. S. Saccucci, "Exponentially weighted moving average control schemes: Properties and enhancements," Technometrics, vol. 32, no. 1, pp. 1-29, 1990.
25. R. L. Mason, N. D. Tracy, and J. C. Young, "A practical approach for interpreting multivariate T 2 control chart signals," J. Quality Technol., vol. 29, no. 4, pp. 396-406, 1997. (Pubitemid 127581421)