Abstract
Predicting the output time of every lot in a semiconductor fabrication factory (wafer fab) is a critical task to the wafer fab. To further enhance the effectiveness of wafer lot output time prediction, a hybrid and intelligent system is constructed in this study. The system is composed of two major parts (a k-means classifier and a back-propagation-network regression) and has three intelligent features: incorporating the future release plan of the fab (look-ahead), example classification, and artificial neural networking. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the hybrid and intelligent system was significantly better than those of four existing approaches: BPN, case-based reasoning (CBR), FBPN, kM-BPN, by achieving a 9%~44% (and an average of 25%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – BPN.
This work was support by the National Science Council, R.O.C.
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References
Barman, S.: The Impact of Priority Rule Combinations on Lateness and Tardiness. IIE Transactions 30, 495–504 (1998)
Chang, P.-C., Hsieh, J.-C.: A Neural Networks Approach for Due-date Assignment in a Wafer Fabrication Factory. International Journal of Industrial Engineering 10(1), 55–61 (2003)
Chang, P.-C., Hsieh, J.-C., Liao, T.W.: A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS, vol. 2080, p. 648. Springer, Heidelberg (2001)
Chang, P.-C., Hsieh, J.-C., Liao, T.W.: Evolving Fuzzy Rules for Due-date Assignment Problem in Semiconductor Manufacturing Factory. Journal of Intelligent Manufacturing 16, 549–557 (2005)
Chen, T.: A Fuzzy Back Propagation Network for Output Time Prediction in a Wafer Fab. Journal of Applied Soft Computing 2/3F, 211–222 (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Lin, C.-Y.: Shop Floor Scheduling of Semiconductor Wafer Fabrication Using Real-time Feedback Control and Prediction. Ph.D. Dissertation, Engineering-Industrial Engineering and Operations Research, University of California at Berkeley (1996)
Vig, M.M., Dooley, K.J.: Dynamic Rules for Due-date Assignment. International Journal of Production Research 29(7), 1361–1377 (1991)
Wang, L.-X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics 22(6), 1414–1427 (1992)
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Chen, T., Lin, YC. (2006). A Hybrid and Intelligent System for Predicting Lot Output Time in a Semiconductor Fabrication Factory. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_78
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DOI: https://doi.org/10.1007/11908029_78
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