Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference

European Journal of Operational Research

Volumn 190, Issue 1, 2008, Pages 228-240

  Yuan, Tao ^a   Kuo, Way ^a  

^a University of Tennessee Knoxville   (United States)

Author keywords

Pattern recognition; Quality control; Semiconductor manufacturing; Stochastic processes

Indexed keywords

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; INFERENCE ENGINES; QUALITY CONTROL; SEMICONDUCTOR MATERIALS; STOCHASTIC MODELS; WAFER BONDING;

SEMICONDUCTOR MANUFACTURING; SEMICONDUCTOR WAFERS;

PATTERN RECOGNITION;

EID: 41149178180     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2007.06.007     Document Type: Article

References (27)

1. Banfield J.D., and Raftery A.E. Ice floe identification in satellite images using mathematical morphology and clustering about principal curves. Journal of the American Statistical Association 87 (1992) 7-16
2. Banfield J.D., and Raftery A.E. Model-based Gaussian and non-Gaussian clustering. Biometrics 49 (1993) 803-821
3. Bensmail H., Celeux G., Raftery A.E., and Robert C.P. Inference in model-based cluster analysis. Statistics and Computing 7 (1997) 1-10
4. Bensmail H., and Meulman J.J. Model-based clustering with noise: Bayesian inference and estimation. Journal of Classification 20 (2003) 49-76
5. Campbell J.G., Fraley C., Murtagh F., and Raftery A.E. Linear flaw detection in woven textiles using model-based clustering. Pattern Recognition Letters 18 (1997) 1539-1548
6. Chen F.-L., and Liu S.-F. A neural-network approach to recognize defect spatial pattern in semiconductor fabrication. IEEE Transactions on Semiconductor Manufacturing 13 (2000) 366-373
7. Daley D.J., and Vere-Jones D. An Introduction to the Theory of Point Processes (1988), Springer
8. Dasgupta A., and Raftery A.E. Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93 (1998) 294-302
9. Diggle P.J. Statistical Analysis of Spatial Point Patterns (1983), Academic Press
10. Figueiredo M.A.T., and Jain A.K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 1-16
11. Fraley C., and Raftery A.E. Model-based clustering, discriminant, analysis, and density estimation. Journal of the American Statistical Association 97 (2002) 611-631
12. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian Data Analysis. second ed. (2003), Chapman & Hall/CRC
13. Gleason S.S., Tobin K.W., Karnowski T.P., and Lakhani F. Rapid yield learning through optical defect and electrical test analysis. Proceedings of SPIE-The International Society for Optical Engineering 3332 (1998) 232-242
14. Hansen C.K., and Thyregod P. Use of wafer maps in integrated circuit manufacturing. Microelectronics Reliability 38 (1998) 1155-1164
15. Hastie T., and Stuetzle W. Principal curves. Journal of the American Statistical Association 84 (1989) 502-516
16. Hwang J.Y., and Kuo W. Model-based clustering for integrated circuit yield enhancement. European Journal of Operational Research 178 (2007) 143-153
17. Hwang JY, Kuo W, Ha C. Yield modeling of integrated circuits based on spatial nonhomogeneous Poisson process: Theory and simulation. Naval Research Logistics (under review).
18. Kuo W., Chien K.W., and Kim T. Reliability, Yield and Stress burn-in: A Unified Approach for Microelectronics Systems Manufacturing and Software Development (1998), Kluwer Academic Publishers
19. Martinez A.M., and VItrià J. Learning mixture models using a genetic version of the EM algorithm. Pattern Recognition Letters 21 (2000) 759-769
20. Pernkopf F., and Bouchaffra D. Genetic-based EM algorithm for learning Gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 1344-1348
21. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
22. Shankar N.G., and Zhong Z.W. Defect detection on semiconductor wafer surfaces. Microelectronics Engineering 77 (2005) 337-346
23. Shankar N.G., and Zhong Z.W. A new rule-based clustering technique for defect analysis. Microelectronics Journal 36 (2005) 718-724
24. Stanford D.C., and Raftery A.E. Finding curvilinear features in spatial point patterns: Principal curve clustering with noise. IEEE Transactions on Pattern and Machine Intelligence 22 (2000) 601-609
25. Ueda N., Nakano R., Ghahramani Z., and Hinton G.E. SMEM algorithm for mixture models. Neural Computation 12 (2000) 2109-2128
26. Wang C.H., Kuo W., and Bensmail H. Detection and classification of defects pattern on semiconductor wafers. IIE Transactions 39 (2006) 1059-1068
27. Yuan, T., Kuo, W. Spatial defect pattern recognition for semiconductor manufacturing via model-based clustering. IIE Transactions (in press).