Paper Title:
Kohonen Neural Network – Based Performance Improvements for Wafer Photolithography Process with CONWIP Control Strategy
  Abstract

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.

  Info
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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