Abstract
Predicting the completion time of a lot is a critical task to a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying a wafer lot before predicting the completion time was beneficial to prediction accuracy. However, most classification approaches applied in this field could not absolutely classify wafer lots. Besides, whether the pre-classification approach combined with the subsequent prediction approach was suitable for the data was questionable. For tackling these two problems, a self-organization map-fuzzy-back-propagation network-ensemble (SOM-FBPN-ensemble) approach with error feedback to adjust classification is proposed in this study. The proposed methodology has two advanced features: predicting the completion time using a FBPN-ensemble instead of a single FBPN, and feeding back the prediction error to adjust the classification result by the SOM. According to experimental results, the prediction accuracy of the proposed approach was significantly better than those of many existing approaches. Besides, the effects of the two advanced features were also evident.
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References
Barman S (1998) The impact of priority rule combinations on lateness and tardiness. IIE Trans 30:495–504
Chang P-C, Chen LY (2006) A hybrid regulation system by evolving CBR with GA for a twin laser measuring system. Int J Adv Manuf Technol, DOI 10.1007/s00170-005-0286-4
Chang P-C, Hsieh J-C (2003) A neural networks approach for due-date assignment in a wafer fabrication factory. Int J Ind Eng 10(1):55–61
Chang P-C, Hsieh J-C, Liao TW (2001) A case-based reasoning approach for due date assignment in a wafer fabrication factory. Proc International Conference on Case-Based Reasoning (ICCBR 2001), Vancouver, British Columbia, Canada
Chang P-C, Hsieh J-C, Liao TW (2005) Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory. J Intell Manuf 16:549–557
Chang P-C, Liao TW (2006) Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl Soft Comput 6:198–206
Chang P-C, Liu CH, Wang YW (2006) A hybrid model by clustering and evolving fuzzy rules for sale forecasting in printed circuit board industry. Decis Support Syst 42(3):1254–1269
Chen T (2003) A fuzzy back propagation network for output time prediction in a wafer fab. Applied Soft Comput 2/3F:211–222
Chen T (in press) A hybrid look-ahead SOM-FBPN and FIR system for wafer lot output time prediction and achievability evaluation. Int J Adv Manuf Technol
Chen T (2006) A hybrid SOM-BPN approach to lot output time prediction in a wafer fab. Neural Process Lett 24(3):271–288
Chen T (2006) A look-ahead fuzzy back propagation network for lot output time series prediction in a wafer fab. Lect Notes Comput Sci 4234:974–982
Chen T (2006) An intelligent hybrid system for wafer lot output time prediction. Adv Eng Inform 21:55–65
Chen T, Jeang A, Wang Y-C (in press) A hybrid neural network and selective allowance approach for internal due date assignment in a wafer fabrication plant. Int J Adv Manuf Technol
Hsu SY, Sha DY (2004) Due date assignment in wafer fabrication using artificial neural network. Int J Adv Manuf Technol 23(9–10):768–775
Tiwari MK, Roy D (2002) Minimization of internal shrinkage in casting using synthesis of neural network. Int J Smart Eng Syst Des 4:205–214
Wang L-X, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427
Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Patt Anal Mach Intell 13:841–847
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Chen, T. A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction. Int J Adv Manuf Technol 37, 782–792 (2008). https://doi.org/10.1007/s00170-007-1007-y
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DOI: https://doi.org/10.1007/s00170-007-1007-y