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Mathematical Problems in Engineering
Volume 2013, Article ID 672404, 7 pages
http://dx.doi.org/10.1155/2013/672404
Research Article

Semiconductor Yield Forecasting Using Quadratic-Programming-Based Fuzzy Collaborative Intelligence Approach

Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City 407, Taiwan

Received 26 March 2013; Accepted 17 May 2013

Academic Editor: Yi-Chi Wang

Copyright © 2013 Toly Chen and Yu-Cheng Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Chen, “A FNP approach for evaluating and enhancing the long-term competitiveness of a semiconductor fabrication factory through yield learning modeling,” International Journal of Advanced Manufacturing Technology, vol. 40, no. 9-10, pp. 993–1003, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Chen, “A fuzzy mid-term single-fab production planning model,” Journal of Intelligent Manufacturing, vol. 14, no. 3-4, pp. 273–285, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Chen and M.-J. J. Wang, “Forecasting methods using fuzzy concepts,” Fuzzy Sets and Systems, vol. 105, no. 3, pp. 339–352, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. A. M. Spence, “The learning curve and competition,” Bell Journal of Economics, vol. 12, pp. 49–70, 1981. View at Google Scholar
  5. S. Majd and R. S. Pindyck, “The learning curve and optimal production under uncertainty,” Rand Journal of Economics, vol. 20, no. 3, pp. 331–343, 1989. View at Google Scholar
  6. J. B. Mazzola and K. F. McCardle, “A Bayesian approach to managing learning-curve uncertainty,” Management Science, vol. 42, no. 5, pp. 680–692, 1996. View at Google Scholar · View at Scopus
  7. K. Anderson, “Innovative yield modeling using statistics,” in Proceedings of the 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference (ASMC '06), pp. 219–221, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Chen and Y. C. Lin, “A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting,” International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 16, no. 1, pp. 35–58, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Chen and Y. C. Wang, “An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting,” IEEE Transactions on Fuzzy Systems, 2013. View at Publisher · View at Google Scholar
  10. J. Watada, H. Tanaka, and T. Shimomura, “Identification of learning curve based on possibilistic concepts,” in Applications of Fuzzy Set Theory in Human Factors, Elsevier, The Netherlands, 1986. View at Google Scholar
  11. J. S. Lin, “Constructing a yield model for integrated circuits based on a novel fuzzy variable of clustered defect pattern,” Expert Systems With Applications, vol. 39, no. 3, pp. 2856–2864, 2012. View at Google Scholar
  12. L. Wu, J. Zhang, and G. Zhang, “A fuzzy neural network approach for die yield prediction of wafer fabrication line,” in Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '09), pp. 198–202, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Y.-M. Chiang and H.-H. Hsieh, “Applying grey relational analysis and fuzzy neural network to improve the yield of thin-film sputtering process in color filter manufacturing,” in Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing (INCOM '09), pp. 408–413, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. W. Pedrycz, “Collaborative architectures of fuzzy modeling,” in Computational Intelligence: Research Frontiers, vol. 5050 of Lecture Notes in Computer Science, pp. 117–139, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  15. T. Chen and Y. C. Wang, “A fuzzy-neural approach for global CO2 concentration forecasting,” Intelligent Data Analysis, vol. 15, no. 5, pp. 763–777, 2011. View at Google Scholar
  16. T. Chen, “A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant,” International Journal of Systems Science, vol. 40, no. 8, pp. 889–902, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. O. Shai and Y. Reich, “Infused design. I. Theory,” Research in Engineering Design, vol. 15, no. 2, pp. 93–107, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. O. Shai and Y. Reich, “Infused design. II. Practice,” Research in Engineering Design, vol. 15, no. 2, pp. 108–121, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Ostrosi, L. Haxhiaj, and S. Fukuda, “Fuzzy modelling of consensus during design conflict resolution,” Research in Engineering Design, pp. 1–18, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. T. S. L. Lo, L. H. S. Luong, and R. M. Marian, “Holistic and collaborative demand forecasting process,” in Proceedings of the IEEE International Conference on Industrial Informatics (INDIN '06), pp. 782–787, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. G. Büyüközkan and Z. Vardaloǧlu, “Analyzing of collaborative planning, forecasting and replenishment approachusing fuzzy cognitive map,” in Proceedings of the International Conference on Computers and Industrial Engineering (CIE '09), pp. 1751–1756, July 2009. View at Scopus
  22. G. B. Büyüközkan, O. Feyzioglu, and Z. Vardaloglu, “Analyzing CPFR supporting factors with fuzzy cognitive map approach,” World Academy of Science, Engineering and Technology, vol. 31, pp. 412–417, 2009. View at Google Scholar
  23. N. Cheikhrouhou, F. Marmier, O. Ayadi, and P. Wieser, “A collaborative demand forecasting process with event-based fuzzy judgements,” Computers and Industrial Engineering, vol. 61, no. 2, pp. 409–421, 2011. View at Google Scholar
  24. R. Poler, J. E. Hernandez, J. Mula, and F. C. Lario, “Collaborative forecasting in networked manufacturing enterprises,” Journal of Manufacturing Technology Management, vol. 19, no. 4, pp. 514–528, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Chen, Y.-C. Wang, and H. C. Wu, “A fuzzy-neural approach for remaining cycle time estimation in a semiconductor manufacturing factory—a simulation study,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 8, pp. 2125–2139, 2009. View at Google Scholar · View at Scopus
  26. T. Chen, “A hybrid look-ahead SOM-FBPN and FIR system for wafer-lot-output time prediction and achievability evaluation,” International Journal of Advanced Manufacturing Technology, vol. 35, no. 5-6, pp. 575–586, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Pedrycz and P. Rai, “A multifaceted perspective at data analysis: a study in collaborative intelligent agents,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 38, no. 4, pp. 1062–1072, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Chen, “A hybrid fuzzy and neural approach with virtual experts and partial consensus for DRAM price forecasting,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 1B, pp. 583–598, 2012. View at Google Scholar
  29. H. Gruber, Learning and Strategic Product Innovation: Theory and Evidence for the Semiconductor Industry, Elsevier, The Netherlands, 1994.