Virtual metrology-based engineering chain management by multi-classification of quality using support vector machine for semiconductor manufacturing

International Journal of Industrial and Systems Engineering

Volumn 8, Issue 1, 2011, Pages 1-18

  Arima, Sumika ^a  

^a UNIVERSITY OF TSUKUBA   (Japan)

Author keywords

ECM; Engineering chain management; Multi classification; Quality management; Quality prediction; Semiconductor manufacturing; Support vector machine; SVM; Systems engineering; Virtual metrology; VM

Indexed keywords

FORECASTING; MILITARY ELECTRONIC COUNTERMEASURES; QUALITY MANAGEMENT; SEMICONDUCTOR DEVICE MANUFACTURE; SYSTEMS ENGINEERING; VIRTUAL MACHINE;

CHAIN MANAGEMENT; MULTI-CLASSIFICATION; QUALITY PREDICTION; SEMICONDUCTOR MANUFACTURING; VIRTUAL METROLOGY;

SUPPORT VECTOR MACHINES;

EID: 79959660390     PISSN: 17485037     EISSN: 17485045     Source Type: Journal    
DOI: 10.1504/IJISE.2011.040763     Document Type: Article

References (28)

1. Bertsimas, D. and Shioda, R. (2007) 'Classification and regression via integer optimization', Operations Research, Vol. 55, No. 2, pp.252-271. (Pubitemid 46663460)
2. Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International.
3. Chen, C-S. (2006) 'Concurrent engineer-to-order operation in the manufacturing engineering contracting industries', Int. J. Industrial and Systems Engineering, Vol. 1, Nos. 1/2, pp.37-58.
4. Chien, C.F., Wang, W.C. and Cheng, J.C. (2007) 'Data mining for yield enhancement in semiconductor manufacturing and an empirical study', Expert Systems with Applications, Vol. 33, No. 1, pp.192-198.
5. Cooke, N.J. (2008) 'Preface of the special 50th anniversary issue of human factors', Human Factors, Vol. 50, No. 3, pp.347-350. (Pubitemid 352029787)
6. Cook, D., et al. (2007) Interactive and Dynamic Graphics for Data analysis: with R and GGobi. New York: Springer.
7. Cristianini, N. and Shawe-Taylor, J.S. (2006) An Introduction to Support Vector Machines (10th printing). New York: Cambridge University Press.
8. Cummings, T.G. and Srivastva, S. (1977) Management of Work. San Diego, CA: University Associates.
9. Friedman, J. (1991) 'Multivariate adaptive regression splines', The Annals of Statistics, Vol. 19, No. 1, pp.1-67.
10. Hong, S.J. and May, G.S. (2004) 'Neural network-based real-time malfunction diagnosis of reactive ion etching using insitu metrology data', IEEE Transactions on Semiconductor Manufacturing, Vol. 17, No. 3, pp.408-421.
11. Hsu, C.W. and Lin, C.J. (2002) 'A comparison of methods for multi-class support vector machines', IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp.415-425.
12. Iino, M., Matsumoto, Y. and Arima, S. (2007) 'Analysis of balance management at the leading edge - competition and collaboration for semiconductor industry', Proceedings of 2007 International Symposium on Semiconductor Manufacturing, IEEE, pp.308-311.
13. Jackson, J.E. (1959) 'Quality control method for several related variables', Technometrics, Vol. 1, No. 4, pp.359-377.
14. Jackson, J.E. (1980) 'Principal component and factor analysis: Part I - principal components', The Journal of Quality Technology, Vol. 12, No. 4, pp.201-213.
15. Jackson, J.E. and Mudholkar, G.S. (1979) 'Control procedures for residuals associated with principal component analysis', Technometrics, Vol. 21, No. 3, pp.341-349. (Pubitemid 9472204)
16. Johnson, R.A. and Wichern, D.W. (2002) Applied Multivariate Statistical Analysis (5th edition). Upper Saddle River, NJ: Prentice-Hall, Inc.
17. Kano, M., Lee, S. and Hasebe, S. (2009) 'Two-stage subspace identification for softsensor design and disturbance estimation', Journal of Process Control, Vol. 19, pp.179-186.
18. Mangasarian, O.L. (1965) 'Linear and nonlinear separation of patterns by linear programming', Operations Research, Vol. 13, No. 3, pp.444-452.
19. Morinaga, H., et al. (2006) 'Development of a platform for collaborative engineering data flow between design and manufacturing', Proceedings of IEEE International Symposium on Semiconductor Manufacturing, IEEE, pp.45-48.
20. Moyne, J., Khan, A. and Tilbury, D. (2006) 'Fab-wide virtual metrology and feedback control', Proceedings of AEC/APC Symposium-Asia, International Sematech Manufacturing Initiative, a33 (in CD-ROM).
21. Olafsson, S. (2006) 'Introduction to operations research and data mining', Computers and Operations Research, Vol. 33, No. 11, pp.3067-3069. (Pubitemid 43333213)
22. Onoda, T., Ito, N. and Yamasaki, H. (2008) 'Unusual condition monitoring based on support vector machines for hydroelectric power plants', Proceedings of IEEE Congress on Evolutionary Computation, IEEE, pp.2254-2261.
23. Rai, B.K. (2009) 'Classification, feature selection and prediction with neural-network Taguchi system', Int. J. Industrial and Systems Engineering, Vol. 4, No. 6, pp.645-664.
24. Rifkin, R. (2002) 'Everything old is new again: A fresh look at historical approaches in machine learning', PhD Thesis, Electrical Engineering and Comuter Science and Operations Research, Massachusetts Institute of Technology, Cambridge, MA.
25. Vapnik, V.N. (1999) The Nature of Statistical Learning Theory. New York: Springer-Verlag.
26. Vicente, K.J. (2006) 'Cognitive engineering a theoretical framework and three case studies', Int. J. Industrial and Systems Engineering, Vol. 1, Nos. 1/2, pp.168-181.
27. Wang, S.C. et.al. (2006) 'The construction and application of a statistical model in virtual metrology', Proceedings of AEC/APC Symposium-Asia, Taiwan, a15 in CD-ROM.
28. Wong, W. and Hsu, S. (2006) 'Application of SVM and ANN for image retrieval', European Journal of Operations Research, Vol. 173, pp.938-950. (Pubitemid 43866336)