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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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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