A hybrid SOM-FBPN approach for output time prediction in a wafer fab

Authors:
Toly Chen
Affiliations:
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan, R.O.C.
Venue:
ROCOM'06 Proceedings of the 6th WSEAS international conference on Robotics, control and manufacturing technology
Year:
2006

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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) in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Examples belonging to different categories are then learned with the same FBPN but with different parameter values. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of two existing approaches, FBPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 15%-45% (and an average of 31%) reduction in the root-mean-squared-error (RMSE).