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Proceedings Paper

Semi-empirical MOCVD modeling using neural networks
Author(s): Ziba Nami; Ahmet Erbil; Gary Stephen May
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Paper Abstract

Metal-organic chemical vapor deposition (MOCVD) is an important fabrication process used to grow thin epitaxial films on solid substrates. The development of an accurate and efficient model for this technique is therefore quite desirable from a manufacturing standpoint. In this paper, semi-empirical modeling of TiO2 film growth by MOCVD using a hybrid neural network is introduced. This hybrid model combines the best aspects of physical models and purely empirical methods. The model was constructed by characterization of the deposition rate of TiO2 films under various operating conditions. A modified back-propagation neural network was trained on the experimental data to determine the value of three critical unknown parameters of the physical model. Using this approach, comparison with measured data showed that the hybrid model is capable of predicting the TiO2 deposition rate with a high degree of accuracy.

Paper Details

Date Published: 14 September 1994
PDF: 9 pages
Proc. SPIE 2334, Microelectronics Manufacturability, Yield, and Reliability, (14 September 1994); doi: 10.1117/12.186761
Show Author Affiliations
Ziba Nami, Georgia Institute of Technology (United States)
Ahmet Erbil, Georgia Institute of Technology (United States)
Gary Stephen May, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2334:
Microelectronics Manufacturability, Yield, and Reliability
Barbara Vasquez; Hisao Kawasaki, Editor(s)

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