A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab
Neural Processing Letters
An intelligent hybrid system for wafer lot output time prediction
Advanced Engineering Informatics
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
Robotics and Computer-Integrated Manufacturing
International Journal of Intelligent Information Technologies
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Lot output time prediction is a critical task to a wafer fabrication plant (wafer fab). To further enhance the accuracy of wafer lot output time prediction, the concept of clustering is applied to Chen's fuzzy back propagation network (FBPN) approach in this study by pre-classifying wafer lots before predicting their output times with several FBPNs that have the same topology. Each wafer lot category has a corresponding FBPN that is applied to predict the output times of all lots belonging to the category. In choosing the learning examples of each category, whether a wafer lot can be unambiguously classified or not and the accuracy of predicting the output time of the lot are simultaneously taken into account. To validate the effectiveness of the proposed methodology and to make comparison with some existing approaches, the actual data in a wafer fab were collected. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of some existing approaches in most cases by achieving a 19-52% (and an average of 38%) reduction in the root-mean-square-error (RMSE). On the other hand, compared with the fuzzy c-means (FCM)-BPN-ensemble approach, the performance of the proposed methodology in the efficiency respect was indeed improved.