Kohonen Neural Network – Based Performance Improvements for Wafer Photolithography Process with CONWIP Control Strategy

Abstract:

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Photolithography is usually the bottleneck process with the most expensive equipment in a semiconductor wafer fabrication system. To improve the performances of the photolithography area with dynamic combination rules, a method of Kohonen neural network (KNN)–based performance improvements is proposed. First, a dynamic scheduling framework based on a KNN model and scheduling rules is proposed. A KNN-based sample learning algorithm for improving the performances is presented. Finally, to demonstrate the validity and feasibility of the proposed method, data from a real wafer fabrication system are used to simulate the proposed method. Results of simulation experiments indicate that the proposed method can be used to improve a complex wafer photolithography performance.

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Periodical:

Edited by:

Ran Chen

Pages:

18-22

DOI:

10.4028/www.scientific.net/AMM.44-47.18

Citation:

B. H. Zhou "Kohonen Neural Network – Based Performance Improvements for Wafer Photolithography Process with CONWIP Control Strategy", Applied Mechanics and Materials, Vols. 44-47, pp. 18-22, 2011

Online since:

December 2010

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$35.00

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