A Novel Chamber Scheduling Method in Etching Tools Using Adaptive Neural Networks

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.

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