Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks

Chang Eun Kim, Pyung Moon, Sungyeon Kim, Hyeon Woo Jang, Jungsik Bang, Jae Min Myoung, Ilgu Yun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present the nonlinear thermal annealing modeling for the electrical properties of ZnO:Ga thin films on annealing temperature and film thickness using neural network based on error backpropagation (BPNN) algorithm and multilayer perceptron (MLP). The thermal annealing process of ZnO:Ga thin films were characterized by general factorial experimental design. To model the nonlinear annealing process, 6 experiments are trained by BPNN which has 2-4-1 structures. The output response models on carrier concentrations, mobility and resistivity of ZnO:Ga thin films trained by BPNN are represented by surface plot of response surface model. The predicted models by training experiments using BPNN were verified by 2 additional experiments not included to the training experiments, and the performance of models is measured by root mean square error (RMSE) and R-square value. Based on the modeling results, neural network can provide sufficient correspondence between the predicted output values and the measured. The annealing process is nonlinear and complex but the output response can be predicted by the neural network model.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009
Pages152-157
Number of pages6
Publication statusPublished - 2009 Dec 1
EventIASTED International Conference on Artificial Intelligence and Applications, AIA 2009 - Innsbruck, Austria
Duration: 2009 Feb 162009 Feb 18

Publication series

NameProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009

Other

OtherIASTED International Conference on Artificial Intelligence and Applications, AIA 2009
CountryAustria
CityInnsbruck
Period09/2/1609/2/18

Fingerprint

Annealing
Neural networks
Thin films
Oxides
Experiments
Backpropagation algorithms
Multilayer neural networks
Mean square error
Design of experiments
Carrier concentration
Film thickness
Hot Temperature
Electric properties
Temperature

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Kim, C. E., Moon, P., Kim, S., Jang, H. W., Bang, J., Myoung, J. M., & Yun, I. (2009). Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009 (pp. 152-157). (Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009).
Kim, Chang Eun ; Moon, Pyung ; Kim, Sungyeon ; Jang, Hyeon Woo ; Bang, Jungsik ; Myoung, Jae Min ; Yun, Ilgu. / Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. 2009. pp. 152-157 (Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009).
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abstract = "In this paper, we present the nonlinear thermal annealing modeling for the electrical properties of ZnO:Ga thin films on annealing temperature and film thickness using neural network based on error backpropagation (BPNN) algorithm and multilayer perceptron (MLP). The thermal annealing process of ZnO:Ga thin films were characterized by general factorial experimental design. To model the nonlinear annealing process, 6 experiments are trained by BPNN which has 2-4-1 structures. The output response models on carrier concentrations, mobility and resistivity of ZnO:Ga thin films trained by BPNN are represented by surface plot of response surface model. The predicted models by training experiments using BPNN were verified by 2 additional experiments not included to the training experiments, and the performance of models is measured by root mean square error (RMSE) and R-square value. Based on the modeling results, neural network can provide sufficient correspondence between the predicted output values and the measured. The annealing process is nonlinear and complex but the output response can be predicted by the neural network model.",
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Kim, CE, Moon, P, Kim, S, Jang, HW, Bang, J, Myoung, JM & Yun, I 2009, Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks. in Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009, pp. 152-157, IASTED International Conference on Artificial Intelligence and Applications, AIA 2009, Innsbruck, Austria, 09/2/16.

Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks. / Kim, Chang Eun; Moon, Pyung; Kim, Sungyeon; Jang, Hyeon Woo; Bang, Jungsik; Myoung, Jae Min; Yun, Ilgu.

Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. 2009. p. 152-157 (Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kim CE, Moon P, Kim S, Jang HW, Bang J, Myoung JM et al. Modeling of thermal annealing of Zno:Ga thin films for transparent conductive oxide using neural networks. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. 2009. p. 152-157. (Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009).