Loading…
Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks
•Modeling of the band gap energy of GaNSbAs by Double Band Anticrossing method.•Modeling of the band gap energy of GaNSbAs by Artificial Neural Networks method.•Prediction of the absorption coefficient of the GaNSbAs deformed by ANN method.•Effect of pressure and temperature on the optical propertie...
Saved in:
Published in: | Materials science & engineering. B, Solid-state materials for advanced technology Solid-state materials for advanced technology, 2023-04, Vol.290, p.116312, Article 116312 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13 |
---|---|
cites | cdi_FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13 |
container_end_page | |
container_issue | |
container_start_page | 116312 |
container_title | Materials science & engineering. B, Solid-state materials for advanced technology |
container_volume | 290 |
creator | Tarbi, Amal Chtouki, Tarek El kouari, Youssef Erguig, Hassane Migalska-Zalas, Anna Aissat, Abdelkader |
description | •Modeling of the band gap energy of GaNSbAs by Double Band Anticrossing method.•Modeling of the band gap energy of GaNSbAs by Artificial Neural Networks method.•Prediction of the absorption coefficient of the GaNSbAs deformed by ANN method.•Effect of pressure and temperature on the optical properties of GaNSbAs deformed.•Optimization of optical properties for optoelectronic applications.
The physical properties of the low bandgap III-V-N-Sb semiconductor elaborated on a GaAs substrate were modeled. The effect of deformation owing to lattice mismatch was considered. The artificial neural network method was used to predict the bandgap energy of the quaternary GaNySbzAs1-y-z, which showed high accuracy (RMSEANN = 0.02 and R2ANN = 99.56 %) and can replace the double band anticrossing model DBAC. The quaternary lattice disagreement, which is rich in Nitrogen and Antimony, makes it possible to design good absorbers. For Sb = 15 %, adding 1 % N increases the absorption coefficient by 1.51 × 104 cm−1. The effects of temperature and pressure on the variation of the absorption coefficient were also studied. The development of the robust artificial neural network based on the Levenberg-Maquardt backpropagation algorithm allows to estimate in a non-linear and precise way the energy of the band gap of the deformed material GaNySbzAs1-y-z, while minimizing the computational cost. |
doi_str_mv | 10.1016/j.mseb.2023.116312 |
format | article |
fullrecord | <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_mseb_2023_116312</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0921510723000545</els_id><sourcerecordid>S0921510723000545</sourcerecordid><originalsourceid>FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEqXwA6z8AykeO60TiQ1CPCJBWfDYWo49AZckrmwXBH_BH5O0rFldaXTPzOgQcgpsBgwWZ6tZF7GeccbFDGAhgO-RCRRSZHmZ5_tkwkoO2RyYPCRHMa4YY8A5n5Cfe2-xdf0r1b2lfp1c5751cr6nvqGP9Xa8pAGj73VvkMakE0aKTYMm0aGW3pDWYyumsDFpE3AkOxc7ncwbWlpVVbbMXqhuW_8V6SZur4XkGmecbmmPm7CN9OnDezwmB41uI5785ZQ8X189Xd5mdw831eXFXWYEYynDxVyyBqUtRImWS24YzI3OwYLkYlHaXBaSzXktylwXkjdFLvNSSwaiBjAgpoTv9prgYwzYqHVwnQ5fCpgapaqVGqWqUaraSR2g8x2Ew2cfDoOKxuHgxbow-FDWu__wXz7kgS8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks</title><source>ScienceDirect Journals</source><creator>Tarbi, Amal ; Chtouki, Tarek ; El kouari, Youssef ; Erguig, Hassane ; Migalska-Zalas, Anna ; Aissat, Abdelkader</creator><creatorcontrib>Tarbi, Amal ; Chtouki, Tarek ; El kouari, Youssef ; Erguig, Hassane ; Migalska-Zalas, Anna ; Aissat, Abdelkader</creatorcontrib><description>•Modeling of the band gap energy of GaNSbAs by Double Band Anticrossing method.•Modeling of the band gap energy of GaNSbAs by Artificial Neural Networks method.•Prediction of the absorption coefficient of the GaNSbAs deformed by ANN method.•Effect of pressure and temperature on the optical properties of GaNSbAs deformed.•Optimization of optical properties for optoelectronic applications.
The physical properties of the low bandgap III-V-N-Sb semiconductor elaborated on a GaAs substrate were modeled. The effect of deformation owing to lattice mismatch was considered. The artificial neural network method was used to predict the bandgap energy of the quaternary GaNySbzAs1-y-z, which showed high accuracy (RMSEANN = 0.02 and R2ANN = 99.56 %) and can replace the double band anticrossing model DBAC. The quaternary lattice disagreement, which is rich in Nitrogen and Antimony, makes it possible to design good absorbers. For Sb = 15 %, adding 1 % N increases the absorption coefficient by 1.51 × 104 cm−1. The effects of temperature and pressure on the variation of the absorption coefficient were also studied. The development of the robust artificial neural network based on the Levenberg-Maquardt backpropagation algorithm allows to estimate in a non-linear and precise way the energy of the band gap of the deformed material GaNySbzAs1-y-z, while minimizing the computational cost.</description><identifier>ISSN: 0921-5107</identifier><identifier>EISSN: 1873-4944</identifier><identifier>DOI: 10.1016/j.mseb.2023.116312</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Artificial intelligence ; Laser ; New materials ; Optoelectronics ; Semiconductor III-V-N-Sb</subject><ispartof>Materials science & engineering. B, Solid-state materials for advanced technology, 2023-04, Vol.290, p.116312, Article 116312</ispartof><rights>2023 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13</citedby><cites>FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Tarbi, Amal</creatorcontrib><creatorcontrib>Chtouki, Tarek</creatorcontrib><creatorcontrib>El kouari, Youssef</creatorcontrib><creatorcontrib>Erguig, Hassane</creatorcontrib><creatorcontrib>Migalska-Zalas, Anna</creatorcontrib><creatorcontrib>Aissat, Abdelkader</creatorcontrib><title>Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks</title><title>Materials science & engineering. B, Solid-state materials for advanced technology</title><description>•Modeling of the band gap energy of GaNSbAs by Double Band Anticrossing method.•Modeling of the band gap energy of GaNSbAs by Artificial Neural Networks method.•Prediction of the absorption coefficient of the GaNSbAs deformed by ANN method.•Effect of pressure and temperature on the optical properties of GaNSbAs deformed.•Optimization of optical properties for optoelectronic applications.
The physical properties of the low bandgap III-V-N-Sb semiconductor elaborated on a GaAs substrate were modeled. The effect of deformation owing to lattice mismatch was considered. The artificial neural network method was used to predict the bandgap energy of the quaternary GaNySbzAs1-y-z, which showed high accuracy (RMSEANN = 0.02 and R2ANN = 99.56 %) and can replace the double band anticrossing model DBAC. The quaternary lattice disagreement, which is rich in Nitrogen and Antimony, makes it possible to design good absorbers. For Sb = 15 %, adding 1 % N increases the absorption coefficient by 1.51 × 104 cm−1. The effects of temperature and pressure on the variation of the absorption coefficient were also studied. The development of the robust artificial neural network based on the Levenberg-Maquardt backpropagation algorithm allows to estimate in a non-linear and precise way the energy of the band gap of the deformed material GaNySbzAs1-y-z, while minimizing the computational cost.</description><subject>Artificial intelligence</subject><subject>Laser</subject><subject>New materials</subject><subject>Optoelectronics</subject><subject>Semiconductor III-V-N-Sb</subject><issn>0921-5107</issn><issn>1873-4944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEqXwA6z8AykeO60TiQ1CPCJBWfDYWo49AZckrmwXBH_BH5O0rFldaXTPzOgQcgpsBgwWZ6tZF7GeccbFDGAhgO-RCRRSZHmZ5_tkwkoO2RyYPCRHMa4YY8A5n5Cfe2-xdf0r1b2lfp1c5751cr6nvqGP9Xa8pAGj73VvkMakE0aKTYMm0aGW3pDWYyumsDFpE3AkOxc7ncwbWlpVVbbMXqhuW_8V6SZur4XkGmecbmmPm7CN9OnDezwmB41uI5785ZQ8X189Xd5mdw831eXFXWYEYynDxVyyBqUtRImWS24YzI3OwYLkYlHaXBaSzXktylwXkjdFLvNSSwaiBjAgpoTv9prgYwzYqHVwnQ5fCpgapaqVGqWqUaraSR2g8x2Ew2cfDoOKxuHgxbow-FDWu__wXz7kgS8</recordid><startdate>202304</startdate><enddate>202304</enddate><creator>Tarbi, Amal</creator><creator>Chtouki, Tarek</creator><creator>El kouari, Youssef</creator><creator>Erguig, Hassane</creator><creator>Migalska-Zalas, Anna</creator><creator>Aissat, Abdelkader</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>202304</creationdate><title>Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks</title><author>Tarbi, Amal ; Chtouki, Tarek ; El kouari, Youssef ; Erguig, Hassane ; Migalska-Zalas, Anna ; Aissat, Abdelkader</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Laser</topic><topic>New materials</topic><topic>Optoelectronics</topic><topic>Semiconductor III-V-N-Sb</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tarbi, Amal</creatorcontrib><creatorcontrib>Chtouki, Tarek</creatorcontrib><creatorcontrib>El kouari, Youssef</creatorcontrib><creatorcontrib>Erguig, Hassane</creatorcontrib><creatorcontrib>Migalska-Zalas, Anna</creatorcontrib><creatorcontrib>Aissat, Abdelkader</creatorcontrib><collection>CrossRef</collection><jtitle>Materials science & engineering. B, Solid-state materials for advanced technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tarbi, Amal</au><au>Chtouki, Tarek</au><au>El kouari, Youssef</au><au>Erguig, Hassane</au><au>Migalska-Zalas, Anna</au><au>Aissat, Abdelkader</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks</atitle><jtitle>Materials science & engineering. B, Solid-state materials for advanced technology</jtitle><date>2023-04</date><risdate>2023</risdate><volume>290</volume><spage>116312</spage><pages>116312-</pages><artnum>116312</artnum><issn>0921-5107</issn><eissn>1873-4944</eissn><abstract>•Modeling of the band gap energy of GaNSbAs by Double Band Anticrossing method.•Modeling of the band gap energy of GaNSbAs by Artificial Neural Networks method.•Prediction of the absorption coefficient of the GaNSbAs deformed by ANN method.•Effect of pressure and temperature on the optical properties of GaNSbAs deformed.•Optimization of optical properties for optoelectronic applications.
The physical properties of the low bandgap III-V-N-Sb semiconductor elaborated on a GaAs substrate were modeled. The effect of deformation owing to lattice mismatch was considered. The artificial neural network method was used to predict the bandgap energy of the quaternary GaNySbzAs1-y-z, which showed high accuracy (RMSEANN = 0.02 and R2ANN = 99.56 %) and can replace the double band anticrossing model DBAC. The quaternary lattice disagreement, which is rich in Nitrogen and Antimony, makes it possible to design good absorbers. For Sb = 15 %, adding 1 % N increases the absorption coefficient by 1.51 × 104 cm−1. The effects of temperature and pressure on the variation of the absorption coefficient were also studied. The development of the robust artificial neural network based on the Levenberg-Maquardt backpropagation algorithm allows to estimate in a non-linear and precise way the energy of the band gap of the deformed material GaNySbzAs1-y-z, while minimizing the computational cost.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.mseb.2023.116312</doi></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0921-5107 |
ispartof | Materials science & engineering. B, Solid-state materials for advanced technology, 2023-04, Vol.290, p.116312, Article 116312 |
issn | 0921-5107 1873-4944 |
language | eng |
recordid | cdi_crossref_primary_10_1016_j_mseb_2023_116312 |
source | ScienceDirect Journals |
subjects | Artificial intelligence Laser New materials Optoelectronics Semiconductor III-V-N-Sb |
title | Modeling and optimization of Sb and N resonance states effect on the band structure of mismatched III-N-V alloys using artificial neural networks |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T22%3A25%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20and%20optimization%20of%20Sb%20and%20N%20resonance%20states%20effect%20on%20the%20band%20structure%20of%20mismatched%20III-N-V%20alloys%20using%20artificial%20neural%20networks&rft.jtitle=Materials%20science%20&%20engineering.%20B,%20Solid-state%20materials%20for%20advanced%20technology&rft.au=Tarbi,%20Amal&rft.date=2023-04&rft.volume=290&rft.spage=116312&rft.pages=116312-&rft.artnum=116312&rft.issn=0921-5107&rft.eissn=1873-4944&rft_id=info:doi/10.1016/j.mseb.2023.116312&rft_dat=%3Celsevier_cross%3ES0921510723000545%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c300t-e6570fe7d839ed272c015ca41d172369d4787052b394a872f84749a7013b11c13%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |