Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms

Young Don Ko; Pyung Moon; Chang Eun Kim; Moon Ho Ham; Jae Min Myoung; Ilgu Yun

doi:10.1016/j.eswa.2008.03.010

Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms

Young Don Ko, Pyung Moon, Chang Eun Kim, Moon Ho Ham, Jae Min Myoung, Ilgu Yun

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

Abstract

The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions.

Original language	English
Pages (from-to)	4061-4066
Number of pages	6
Journal	Expert Systems with Applications
Volume	36
Issue number	2 PART 2
DOIs	https://doi.org/10.1016/j.eswa.2008.03.010
Publication status	Published - 2009 Mar

Bibliographical note

Funding Information:
This work was supported by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 program, Korea and by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) (No. R01-2007-000-20143-0).

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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title = "Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms",

abstract = "The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions.",

author = "Ko, {Young Don} and Pyung Moon and Kim, {Chang Eun} and Ham, {Moon Ho} and Myoung, {Jae Min} and Ilgu Yun",

note = "Funding Information: This work was supported by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 program, Korea and by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) (No. R01-2007-000-20143-0).",

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AU - Moon, Pyung

AU - Kim, Chang Eun

AU - Ham, Moon Ho

AU - Myoung, Jae Min

AU - Yun, Ilgu

N1 - Funding Information: This work was supported by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 program, Korea and by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) (No. R01-2007-000-20143-0).

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N2 - The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions.

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Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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