NDLI: A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab

Content Provider	Springer Nature Link
Author	Chen, Toly
Copyright Year	2006
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 the back propagation network (BPN) approach in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the BPN. Examples belonging to different categories are then learned with different BPNs but with the same topology. 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 three existing approaches, case-based reasoning (CBR), BPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 13–46% (and an average of 30%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – BPN without example classification.
Starting Page	271
Ending Page	288
Page Count	18
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	24
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2006-09-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	back propagation network hybrid approach output time prediction self-organization map wafer fab Operations Research/Decision Theory Electronic and Computer Engineering Artificial Intelligence (incl. Robotics) Complexity
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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