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

Bing Hai Zhou

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

Paper Titles

Preface, Committees and Overview

Application of KingView and PLC in Tailing Cementation-Filling Automatic Control System
p.3

Advanced Low-Carbon Technologies for Steel Manufacturing Process
p.8

Study on the Credit Classification of Practicing Qualification Personnel in Construction Market Based on PNN
p.13

Kohonen Neural Network – Based Performance Improvements for Wafer Photolithography Process with CONWIP Control Strategy
p.18

Research on Control Performance of Micro-Milling System Based on Linear Motor
p.23

Research on Deep Processing of Primary Products Based on DEA and Self-Adaptive Filter Forecasting
p.28

A Mathematical Modeling of Heat Transfer in Continuous Casting Slab
p.33

On Construction Mode for Training Electromechanical Professional Driven by Projects
p.38

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 44-47Kohonen Neural Network – Based Performance...

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.