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Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks

Appraising the bandgap energy of materials is a major issue in the field of band engineering. To better understand the behavior of GaAs(1-u)N(u) material, it is necessary to improve the applied calculation methodologies. The band anticrossing model (BAC) allows modeling of the bandgap energy when di...

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Detalles Bibliográficos
Autores principales: Tarbi, A., Chtouki, T., Elkouari, Y., Erguig, H., Migalska-Zalas, A., Aissat, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418364/
https://www.ncbi.nlm.nih.gov/pubmed/36039133
http://dx.doi.org/10.1016/j.heliyon.2022.e10212
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author Tarbi, A.
Chtouki, T.
Elkouari, Y.
Erguig, H.
Migalska-Zalas, A.
Aissat, A.
author_facet Tarbi, A.
Chtouki, T.
Elkouari, Y.
Erguig, H.
Migalska-Zalas, A.
Aissat, A.
author_sort Tarbi, A.
collection PubMed
description Appraising the bandgap energy of materials is a major issue in the field of band engineering. To better understand the behavior of GaAs(1-u)N(u) material, it is necessary to improve the applied calculation methodologies. The band anticrossing model (BAC) allows modeling of the bandgap energy when diluted nitrogen is incorporated into the material. The model can be improved using artificial neural networks (ANN) as an alternative solution, which is rarely applied. Our goal is to study the efficiency of the (ANN) method to gauge the bandgap energy of the material from experimental measurements, considering the extensive strain due to the lattice mismatch between the substrate and the material. This makes the GaAsN material controllable with (ANN) method, and is a potential candidate for the fabrication of ultrafast optical sensors.
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spelling pubmed-94183642022-08-28 Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks Tarbi, A. Chtouki, T. Elkouari, Y. Erguig, H. Migalska-Zalas, A. Aissat, A. Heliyon Research Article Appraising the bandgap energy of materials is a major issue in the field of band engineering. To better understand the behavior of GaAs(1-u)N(u) material, it is necessary to improve the applied calculation methodologies. The band anticrossing model (BAC) allows modeling of the bandgap energy when diluted nitrogen is incorporated into the material. The model can be improved using artificial neural networks (ANN) as an alternative solution, which is rarely applied. Our goal is to study the efficiency of the (ANN) method to gauge the bandgap energy of the material from experimental measurements, considering the extensive strain due to the lattice mismatch between the substrate and the material. This makes the GaAsN material controllable with (ANN) method, and is a potential candidate for the fabrication of ultrafast optical sensors. Elsevier 2022-08-13 /pmc/articles/PMC9418364/ /pubmed/36039133 http://dx.doi.org/10.1016/j.heliyon.2022.e10212 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Tarbi, A.
Chtouki, T.
Elkouari, Y.
Erguig, H.
Migalska-Zalas, A.
Aissat, A.
Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title_full Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title_fullStr Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title_full_unstemmed Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title_short Bandgap energy modeling of the deformed ternary GaAs(1-u)N(u) by artificial neural networks
title_sort bandgap energy modeling of the deformed ternary gaas(1-u)n(u) by artificial neural networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418364/
https://www.ncbi.nlm.nih.gov/pubmed/36039133
http://dx.doi.org/10.1016/j.heliyon.2022.e10212
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