Optical constants characterization of As30 Se70?x Snx thin films using neural networks

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Attia A. Attia
Mohammed S. El-Bana
Doaa M. Habashy
Suzan S. Fouad
Mahmoud Y. El-Bakry

Abstract

This paper uses an artificial neural network (ANN) and resilient back-propagation (Rprop) training algorithm to determine the optical constants of As30Se70?xSnx (0 ? x ? 3) thin films. The simulated values of the ANN are in good agreement with the experimental data. The ANN models performance was also examined to predict the simulated values for As30 Se67 Sn3 which was not included in the training and was found to be in accordance with the experimental data. The high precision of the ANN models as well as a great guessing performance have been exhibited. Moreover, the energy gap Eg of As30Se70?x Snx (0 ? x ? 9) thin films were calculated theoretically.

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How to Cite
Attia, A. A., El-Bana, M. S., Habashy, D. M., Fouad, S. S., & El-Bakry, M. Y. (2019). Optical constants characterization of As30 Se70?x Snx thin films using neural networks. Journal of Applied Research and Technology, 15(5). https://doi.org/10.1016/j.jart.2017.03.009
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Articles
Author Biographies

Attia A. Attia

Thin Films Laboratory, Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt

Mohammed S. El-Bana

Nano-Science & Semiconductor Laboratories, Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt

College of Science and Art at Rass-Qassim University, PO Box 53 Postcode 51921, Saudi Arabia

Doaa M. Habashy

Theoretical Group, Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt

Suzan S. Fouad

Nano-Science & Semiconductor Laboratories, Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt

Mahmoud Y. El-Bakry

Theoretical Group, Department of Physics, Faculty of Education, Ain Shams University, Cairo, Egypt