Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm

Edward Namkyu Cho; Pyung Moon; Chang Eun Kim; Ilgu Yun

doi:10.1016/j.eswa.2012.02.019

Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm

Edward Namkyu Cho, Pyung Moon, Chang Eun Kim, Ilgu Yun

Department of Electrical and Electronic Engineering

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

20 Citations (Scopus)

Abstract

In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space.

Original language	English
Pages (from-to)	8885-8889
Number of pages	5
Journal	Expert Systems with Applications
Volume	39
Issue number	10
DOIs	https://doi.org/10.1016/j.eswa.2012.02.019
Publication status	Published - 2012 Aug

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2012.02.019

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title = "Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm",

abstract = "In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space.",

author = "Cho, {Edward Namkyu} and Pyung Moon and Kim, {Chang Eun} and Ilgu Yun",

year = "2012",

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doi = "10.1016/j.eswa.2012.02.019",

language = "English",

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journal = "Expert Systems with Applications",

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T1 - Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm

AU - Cho, Edward Namkyu

AU - Moon, Pyung

AU - Kim, Chang Eun

AU - Yun, Ilgu

PY - 2012/8

Y1 - 2012/8

N2 - In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space.

AB - In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space.

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M3 - Article

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Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm

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