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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-9418364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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