Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-25T02:04:08.890Z Has data issue: false hasContentIssue false

A Calculation Method of Deposition Profiles in Chemical Vapor Deposition Reactors Using Genetic Algorithms for The Automatic Modeling System of Reaction Mechanisms

Published online by Cambridge University Press:  26 February 2011

Takahiro Takahashi
Affiliation:
tttakah@ipc.shizuoka.ac.jp, Shizuoka University, 3-5-1 Johoku, Hamamatsu, Shizuoka, 4328561, Japan
Yoshinori Ema
Affiliation:
teyema@ipc.shizuoka.ac.jp, Japan
Get access

Abstract

Fast and accurate calculation of the predicted results of Chemical Vapor Depositions (CVD) is very helpful to the high-throughput optimization of the CVD processes. In addition, robustness of the calculation process is important for automation of the optimization process. Therefore, we have developed a novel calculation method, by which robust and accurate calculations along with reduced computing cost were achieved, to reproduce deposition profiles in a macroscopic cavity (macrocavity). Boundary value problems for estimating diffusion-reaction equations by iterations of numerical integrations were changed into problems of finding the linear combinations consisted of a few functions. The coefficients of the linear combinations were optimized by Genetic Algorithms (GA). We could demonstrate the validity of the proposed method using various examples of the reaction mechanisms and conditions.

Type
Research Article
Copyright
Copyright © Materials Research Society 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Komiyama, H., Shimogaki, Y. and Egashira, Y., Chem. Eng. Sci. 54, 1941 (1999).Google Scholar
2. Takahashi, T., Funatsu, K. and Ema, Y., Proceedings of Chemical Vapor Deposition XVI and EUROCVD 14, Electrochemical Society, Vol.2, pp. 272278 (2003).Google Scholar
3. Takahashi, T., Funatsu, K. and Ema, Y. in Combinatorial and Artificial Intelligence Methods in Materials Science II, edited by Potyrailo, R.A., Karim, A., Wang, Q. and Chikyow, T., (Mater. Res. Soc. Symp. Proc. 804, Warrendale, PA, 2004) pp. 5762.Google Scholar
4. Takahashi, T., Funatsu, K. and Ema, Y., Meas. Sci. Technol. 16, 278 (2005).Google Scholar
5. Takahashi, T., Takahashi, K. and Ema, Y., Proceedings of EUROCVD-15, Electrochemical Society, pp. 2128 (2005).Google Scholar
6. Watanabe, K. and Komiyama, H., J. Electrochem. Soc. 137, 1222 (1990).Google Scholar
7. Holland, J. H., “Adaptation in natural and artificial systems”, University of Michigan Press (1975).Google Scholar
8. Wehrens, R. and Buydens, L.M.C., Tr. Anal. Chem. 17, 193 (1998).Google Scholar