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Application of a Neural Manufacturing Concept to Process Modeling, Monitoring and Control

Published online by Cambridge University Press:  15 February 2011

Chi Yung Fu ,
Loren Petrich  and
Benjamin Law
Chi Yung Fu
Affiliation:
Lawrence Livermore National Laboratory, University of California, P.O. Box 808, L-271, Livermore, CA 94550, Email: ful@llnl.gov.
Loren Petrich
Affiliation:
Lawrence Livermore National Laboratory, University of California, P.O. Box 808, L-271, Livermore, CA 94550, Email: ful@llnl.gov.
Benjamin Law
Affiliation:
Lawrence Livermore National Laboratory, University of California, P.O. Box 808, L-271, Livermore, CA 94550, Email: ful@llnl.gov.
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Abstract

The cost of a fabrication line, such as one in a semiconductor house, has increased dramatically over the years, and it is possibly already past the point that some new start-up company can have sufficient capital to build a new fabrication line. Such capital-intensive manufacturing needs better utilization of resources and management of equipment to maximize its productivity. In order to maximize the return from such a capital-intensive manufacturing line, we need to work on the following: 1) increasing the yield, 2) enhancing the flexibility of the fabrication line, 3) improving quality, and finally 4) minimizing the down time of the processing equipment. Because of the significant advances now made in the fields of artificial neural networks, fuzzy logic, machine learning and genetic algorithms, we advocate the use of these new tools in manufacturing. We term the applications to manufacturing of these and other such tools that mimic human intelligence neural manufacturing. This paper describes the effort at the Lawrence Livermore National Laboratory (LLNL) [1] to use artificial neural networks to address certain semiconductor process modeling, monitoring and control questions.*

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 389: Symposium R – Modedling and Simulation of Thin-Film Processing , 1995 , 333
Copyright
Copyright © Materials Research Society 1995

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References

REFERENCES

1 Fu, C. Y., Petrich, L., and Law, B., “A Novel Process Monitoring and Control System Based on a Neural Manufacturing Concept,” GOMAC Conference, Las Vegas, NV, Nov. 9–12, 1992.Google Scholar
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