The Estimation of Thin Film Properties by Neural Network

Chi-Yen Shen; Yu-Ju Chen; Shuming T. Wang; Chuo-Yean Chang; Rey-Chue Hwang

doi:doi:10.11648/j.acis.20160402.12

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The Estimation of Thin Film Properties by Neural Network

Chi-Yen Shen, Yu-Ju Chen, Shuming T. Wang, Chuo-Yean Chang, Rey-Chue Hwang

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 2)

Received: 24 March 2016 Published: 25 March 2016

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Abstract

This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.

Published in	Automation, Control and Intelligent Systems (Volume 4, Issue 2)
DOI	10.11648/j.acis.20160402.12
Page(s)	15-20
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Neural Network, Thin Film, Manufacturing Process

References

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APA Style

Chi-Yen Shen, Yu-Ju Chen, Shuming T. Wang, Chuo-Yean Chang, Rey-Chue Hwang. (2016). The Estimation of Thin Film Properties by Neural Network. Automation, Control and Intelligent Systems, 4(2), 15-20. https://doi.org/10.11648/j.acis.20160402.12

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ACS Style

Chi-Yen Shen; Yu-Ju Chen; Shuming T. Wang; Chuo-Yean Chang; Rey-Chue Hwang. The Estimation of Thin Film Properties by Neural Network. Autom. Control Intell. Syst. 2016, 4(2), 15-20. doi: 10.11648/j.acis.20160402.12

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AMA Style

Chi-Yen Shen, Yu-Ju Chen, Shuming T. Wang, Chuo-Yean Chang, Rey-Chue Hwang. The Estimation of Thin Film Properties by Neural Network. Autom Control Intell Syst. 2016;4(2):15-20. doi: 10.11648/j.acis.20160402.12

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@article{10.11648/j.acis.20160402.12,
  author = {Chi-Yen Shen and Yu-Ju Chen and Shuming T. Wang and Chuo-Yean Chang and Rey-Chue Hwang},
  title = {The Estimation of Thin Film Properties by Neural Network},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {15-20},
  doi = {10.11648/j.acis.20160402.12},
  url = {https://doi.org/10.11648/j.acis.20160402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.12},
  abstract = {This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.},
 year = {2016}
}

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TY  - JOUR
T1  - The Estimation of Thin Film Properties by Neural Network
AU  - Chi-Yen Shen
AU  - Yu-Ju Chen
AU  - Shuming T. Wang
AU  - Chuo-Yean Chang
AU  - Rey-Chue Hwang
Y1  - 2016/03/25
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.12
DO  - 10.11648/j.acis.20160402.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 15
EP  - 20
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.12
AB  - This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.
VL  - 4
IS  - 2
ER  -

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Author Information

Chi-Yen Shen

Electrical Engineering Department, I-Shou University, Kaohsiung City, Taiwan
Yu-Ju Chen

Information Management Department, Cheng-Shiu University, Kaohsiung City, Taiwan
Shuming T. Wang

Electrical Engineering Department, I-Shou University, Kaohsiung City, Taiwan
Chuo-Yean Chang

Electrical Engineering Department, Cheng-Shiu University, Kaohsiung City, Taiwan
Rey-Chue Hwang

Electrical Engineering Department, I-Shou University, Kaohsiung City, Taiwan

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Plain Text BibTeX RIS

APA Style

Chi-Yen Shen, Yu-Ju Chen, Shuming T. Wang, Chuo-Yean Chang, Rey-Chue Hwang. (2016). The Estimation of Thin Film Properties by Neural Network. Automation, Control and Intelligent Systems, 4(2), 15-20. https://doi.org/10.11648/j.acis.20160402.12

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ACS Style

Chi-Yen Shen; Yu-Ju Chen; Shuming T. Wang; Chuo-Yean Chang; Rey-Chue Hwang. The Estimation of Thin Film Properties by Neural Network. Autom. Control Intell. Syst. 2016, 4(2), 15-20. doi: 10.11648/j.acis.20160402.12

Copy | Download

AMA Style

Chi-Yen Shen, Yu-Ju Chen, Shuming T. Wang, Chuo-Yean Chang, Rey-Chue Hwang. The Estimation of Thin Film Properties by Neural Network. Autom Control Intell Syst. 2016;4(2):15-20. doi: 10.11648/j.acis.20160402.12

Copy | Download

@article{10.11648/j.acis.20160402.12,
  author = {Chi-Yen Shen and Yu-Ju Chen and Shuming T. Wang and Chuo-Yean Chang and Rey-Chue Hwang},
  title = {The Estimation of Thin Film Properties by Neural Network},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {15-20},
  doi = {10.11648/j.acis.20160402.12},
  url = {https://doi.org/10.11648/j.acis.20160402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.12},
  abstract = {This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - The Estimation of Thin Film Properties by Neural Network
AU  - Chi-Yen Shen
AU  - Yu-Ju Chen
AU  - Shuming T. Wang
AU  - Chuo-Yean Chang
AU  - Rey-Chue Hwang
Y1  - 2016/03/25
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.12
DO  - 10.11648/j.acis.20160402.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 15
EP  - 20
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.12
AB  - This paper presents a method based on neural network (NN) for estimating the properties of semiconductor thin film. Through the effective learning process, NN is able to catch the relationship between input and output pairs bypassing the complicated statistical steps such as model hypothesis, identification, estimation of model parameters, and verification. Such an estimator then can be developed to be a smart mechanism which can help the technician to set the relevant control parameters in the manufacturing process of thin film. In this research, the thickness and refractive index (RI) of thin film were estimated by the well learned NN model. From the studied results shown, the properties of thin film indeed could be estimated in advance according to the relevant control parameters in the manufacturing process. That also means the estimator we developed could be built and fulfilled its function.
VL  - 4
IS  - 2
ER  -

Copy | Download