A Hybrid SOM-FBPN Approach for Output Time Prediction in a Wafer Fab

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). Key-Words: Output time prediction; Fuzzy back propagation network; Self-organization map; Wafer fab