Chamber scheduling in etching tools is an important but difficult task in integrated circuit manufacturing. In order to effectively solve such combinatorial optimization problems in etching tools, this paper presents a novel chamber scheduling approach on the base of Adaptive Artificial Neural Networks (ANNs). Feed forward, multi-layered neural network meta-models were trained through the back-error-propagation (BEP) learning algorithm to provide a versatile job-shop scheduling analysis framework. At the same time, an adaptive selection mechanism has been extended into ANN. By testing the practical data set, the method is able to provide near-optimal solutions for practical chamber scheduling problems, and the results are superior to those generated by what have been reported in the neural network scheduling literature. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Xu, H., Jia, P., & Zhang, X. (2005). A novel chamber scheduling method in etching tools using Adaptive Neural Networks. In Lecture Notes in Computer Science (Vol. 3498, pp. 908–913). Springer Verlag. https://doi.org/10.1007/11427469_144
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