Modeling of In2O3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks

Chang Eun Kim, Hyun Soo Shin, Pyung Moon, Hyun Jae Kim, Ilgu Yun

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Effects of deposition process parameters on the deposition rate and the electrical properties of In2O3-10 wt% ZnO (IZO) thin films were modeled and analyzed by using the error back-propagation neural networks (BPNN). Output models were represented by response surface plots and the fitness of models was estimated by calculating the root mean square error (RMSE). The deposition rate of IZO thin films is affected by the RF power and the substrate temperature. The electrical properties of the IZO thin films are mainly controlled by O2 ratio and the substrate temperature. The predicted output characteristics by BPNN can sufficiently explain the mechanism of IZO deposition process. Thus, neural network models can provide the reliable explanation of IZO film deposition.

Original languageEnglish
Pages (from-to)1407-1410
Number of pages4
JournalCurrent Applied Physics
Volume9
Issue number6
DOIs
Publication statusPublished - 2009 Nov 1

Fingerprint

Oxides
Deposition rates
Neural networks
Backpropagation
Thin films
oxides
Electric properties
thin films
Substrates
Mean square error
electrical properties
fitness
root-mean-square errors
output
Temperature
plots
temperature

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Physics and Astronomy(all)

Cite this

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title = "Modeling of In2O3-10 wt{\%} ZnO thin film properties for transparent conductive oxide using neural networks",
abstract = "Effects of deposition process parameters on the deposition rate and the electrical properties of In2O3-10 wt{\%} ZnO (IZO) thin films were modeled and analyzed by using the error back-propagation neural networks (BPNN). Output models were represented by response surface plots and the fitness of models was estimated by calculating the root mean square error (RMSE). The deposition rate of IZO thin films is affected by the RF power and the substrate temperature. The electrical properties of the IZO thin films are mainly controlled by O2 ratio and the substrate temperature. The predicted output characteristics by BPNN can sufficiently explain the mechanism of IZO deposition process. Thus, neural network models can provide the reliable explanation of IZO film deposition.",
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Modeling of In2O3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks. / Kim, Chang Eun; Soo Shin, Hyun; Moon, Pyung; Jae Kim, Hyun; Yun, Ilgu.

In: Current Applied Physics, Vol. 9, No. 6, 01.11.2009, p. 1407-1410.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modeling of In2O3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks

AU - Kim, Chang Eun

AU - Soo Shin, Hyun

AU - Moon, Pyung

AU - Jae Kim, Hyun

AU - Yun, Ilgu

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AB - Effects of deposition process parameters on the deposition rate and the electrical properties of In2O3-10 wt% ZnO (IZO) thin films were modeled and analyzed by using the error back-propagation neural networks (BPNN). Output models were represented by response surface plots and the fitness of models was estimated by calculating the root mean square error (RMSE). The deposition rate of IZO thin films is affected by the RF power and the substrate temperature. The electrical properties of the IZO thin films are mainly controlled by O2 ratio and the substrate temperature. The predicted output characteristics by BPNN can sufficiently explain the mechanism of IZO deposition process. Thus, neural network models can provide the reliable explanation of IZO film deposition.

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