Abstract
Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to Chen’s fuzzy back propagation network (FBPN) approach in this study by pre-classifying input examples with the k-means (kM) classifier before they are fed into the FBPN. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the intelligent neural system was significantly better than those of four existing approaches: BPN, case-based reasoning (CBR), FBPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 11%~46% (and an average of 31%) 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. (2006). Applying an Intelligent Neural System to Predicting Lot Output Time in a Semiconductor Fabrication Factory. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_64
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DOI: https://doi.org/10.1007/11893295_64
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