NDLI: Semi-empirical neural network modeling of metal-organic chemical vapor deposition

Content Provider	IEEE Xplore Digital Library
Author	Nami, Z. Misman, O. Erbil, A. May, G.S.
Copyright Year	1988
Abstract	Metal-organic chemical vapor deposition (MOCVD) is an important technique for growing thin films with various applications in electronics and optics. The development of accurate and efficient MOCVD process models is therefore desirable, since such models ran be instrumental in improving process control in a manufacturing environment. This paper presents a semi-empirical MOCVD model based on "hybrid" neural networks. The model is constructed by characterizing the MOCVD of titanium dioxide (TiO/sub 2/) films through the measurement of deposition rate over a range of deposition conditions by a statistically designed experiment in which susceptor and source temperature, flow rate of the carrier gas for the precursor and chamber pressure are varied. A modified backpropagation neural network is then trained on the experimental data to determine the value of the adjustable parameters in an analytical expression for the TiO/sub 2/ deposition rate. In so doing, a general purpose methodology for deriving semi-empirical neural process models which take into account prior knowledge of the underlying process physics is developed.
Sponsorship	IEEE Electron Devices Society IEEE Components, Packaging, and Manufacturing Technology Society IEEE Reliability Society IEEE Solid-State Circuits Society
Starting Page	288
Ending Page	294
Page Count	7
File Size	177304
File Format	PDF
ISSN	08946507
Volume Number	10
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks MOCVD Chemical vapor deposition Sputtering Optical computing Optical films Radio access networks Instruments Process control Manufacturing processes
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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