Abstract
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.
This work was jointly supported by the National Nature Science Foundation for Youth Fund (Grant No: 60405011), the China Postdoctoral Foundation for China Postdoctoral Science Fund (Grant No: 20040350078) and the National 863 Project of China.
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Xu, H., Jia, P., Zhang, X. (2005). A Novel Chamber Scheduling Method in Etching Tools Using Adaptive Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_144
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DOI: https://doi.org/10.1007/11427469_144
Publisher Name: Springer, Berlin, Heidelberg
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