Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process

Abstract:

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Photolithography area is usually a bottleneck area in a semiconductor wafer manufacturing system (SWMS). It is difficult to schedule photolithography area on real-time optimally. Here, an Elman neural network (ENN)-based dynamic scheduling method is proposed. An ENN-based sample learning algorithm is proposed for selecting best combination of scheduling rules. To illustrate the feasibility and practicality of the presented method, the simulation experiment is developed. A numerical example is use to evaluate the proposed method. Results of simulation experiments show that the proposed method is effective to schedule a complex wafer photolithography process.

Info:

Periodical:

Edited by:

Wenya Tian and Linli Xu

Pages:

36-40

DOI:

10.4028/www.scientific.net/AMR.186.36

Citation:

B. H. Zhou "Elman Neural Network–Based Dynamic Scheduling of Wafer Photolithography Process", Advanced Materials Research, Vol. 186, pp. 36-40, 2011

Online since:

January 2011

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Price:

$35.00

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