Semi-empirical MOCVD modeling using neural networks

Ziba Nami; Ahmet Erbil; Gary Stephen May

doi:10.1117/12.186761

14 September 1994 Semi-empirical MOCVD modeling using neural networks

Ziba Nami, Ahmet Erbil, Gary Stephen May

Proceedings Volume 2334, Microelectronics Manufacturability, Yield, and Reliability; (1994) https://doi.org/10.1117/12.186761
Event: Microelectronic Manufacturing, 1994, Austin, TX, United States

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.

Citation Download Citation

Ziba Nami, Ahmet Erbil, and Gary Stephen May "Semi-empirical MOCVD modeling using neural networks", Proc. SPIE 2334, Microelectronics Manufacturability, Yield, and Reliability, (14 September 1994); https://doi.org/10.1117/12.186761

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available