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

doi:10.1016/j.cap.2009.03.013

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

Department of Electrical and Electronic Engineering

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

8 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 language	English
Pages (from-to)	1407-1410
Number of pages	4
Journal	Current Applied Physics
Volume	9
Issue number	6
DOIs	https://doi.org/10.1016/j.cap.2009.03.013
Publication status	Published - 2009 Nov

Bibliographical note

Funding Information:
This work was supported 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

Materials Science(all)
Physics and Astronomy(all)

Access to Document

10.1016/j.cap.2009.03.013

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.",

author = "Kim, {Chang Eun} and {Soo Shin}, Hyun and Pyung Moon and {Jae Kim}, Hyun and Ilgu Yun",

note = "Funding Information: This work was supported 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 - Kim, Chang Eun

AU - Soo Shin, Hyun

AU - Moon, Pyung

AU - Jae Kim, Hyun

AU - Yun, Ilgu

N1 - Funding Information: This work was supported 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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