Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm

Lattice-mismatched InGaAs has appeared to be emerging semiconductor materials for sensors and photovoltaic applications. The absorption coefficients of the materials are crucial in designing high-performance semiconductor devices. Nevertheless, the absorption coefficient of lattice-mismatched InGaAs...

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Main Authors: Lee H.J., Ali Gamel M.M., Ker P.J., Jamaludin M.Z., Wong Y.H., Yap K.S., Willmott J.R., Hobbs M.J., David J.P.R., Tan C.H.
Other Authors: 57190622221
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Published: Elsevier Ltd 2024
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spelling my.uniten.dspace-347242024-10-14T11:22:05Z Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm Lee H.J. Ali Gamel M.M. Ker P.J. Jamaludin M.Z. Wong Y.H. Yap K.S. Willmott J.R. Hobbs M.J. David J.P.R. Tan C.H. 57190622221 57280351300 37461740800 57216839721 36605495300 24448864400 55697476100 37116995800 25647614700 57189468185 Absorption Algorithm Coefficient Genetic InGaAs Lattice-mismatched Extraction Gallium alloys Genetic algorithms Indium alloys Infrared radiation Iterative methods Semiconducting indium Semiconducting indium gallium arsenide Semiconductor alloys Semiconductor devices Absorption coefficients Coefficient Genetic High-performance semiconductors Lattice-mismatched Photocurrent measurement Photovoltaic applications Sensor applications Structure design Structure materials Energy gap Lattice-mismatched InGaAs has appeared to be emerging semiconductor materials for sensors and photovoltaic applications. The absorption coefficients of the materials are crucial in designing high-performance semiconductor devices. Nevertheless, the absorption coefficient of lattice-mismatched InGaAs were not comprehensively studied to cater for the 2000�3000 nm applications. This study aims to determine the absorption coefficients of lattice-mismatched In0.73Ga0.27As and In0.83Ga0.17As semiconductor materials through photocurrent measurement which enables the absorption tail information to be extracted. In addition, this work demonstrates the incorporation of an innovative artificial intelligence-based method in solving the absorption coefficient of lattice-mismatched InGaAs, considering the detailed information of the structure design and material parameters. By selecting the best gene for the next iteration, the utilization of Genetic Algorithm has significantly reduced the number of iterations from a maximum of 10 000 to 300. Validation of the algorithm was conducted, showing a good agreement of absorption coefficient result compared to the published work on In0.72Ga0.28As. The absorption coefficient of In0.83Ga0.17As with an extended cutoff wavelength near 2.6 ?m is newly reported in this paper. In addition, the extrapolation of the obtained absorption results demonstrates energy gaps of 0.475 eV for In0.73Ga0.27As and 0.55 eV for In0.83Ga0.17As, which are compatible with the reported bandgaps of these materials. The extracted absorption coefficient information can be used in the design of semiconductor devices for emerging technologies such as focal plane array, short wave infrared sensing and thermophotovoltaic. � 2022 Elsevier Ltd Final 2024-10-14T03:22:05Z 2024-10-14T03:22:05Z 2023 Article 10.1016/j.mssp.2022.107135 2-s2.0-85139333714 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139333714&doi=10.1016%2fj.mssp.2022.107135&partnerID=40&md5=44f3f070a06b8e1fbe33f86557f4e4ff https://irepository.uniten.edu.my/handle/123456789/34724 153 107135 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
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topic Absorption
Algorithm
Coefficient
Genetic
InGaAs
Lattice-mismatched
Extraction
Gallium alloys
Genetic algorithms
Indium alloys
Infrared radiation
Iterative methods
Semiconducting indium
Semiconducting indium gallium arsenide
Semiconductor alloys
Semiconductor devices
Absorption coefficients
Coefficient
Genetic
High-performance semiconductors
Lattice-mismatched
Photocurrent measurement
Photovoltaic applications
Sensor applications
Structure design
Structure materials
Energy gap
spellingShingle Absorption
Algorithm
Coefficient
Genetic
InGaAs
Lattice-mismatched
Extraction
Gallium alloys
Genetic algorithms
Indium alloys
Infrared radiation
Iterative methods
Semiconducting indium
Semiconducting indium gallium arsenide
Semiconductor alloys
Semiconductor devices
Absorption coefficients
Coefficient
Genetic
High-performance semiconductors
Lattice-mismatched
Photocurrent measurement
Photovoltaic applications
Sensor applications
Structure design
Structure materials
Energy gap
Lee H.J.
Ali Gamel M.M.
Ker P.J.
Jamaludin M.Z.
Wong Y.H.
Yap K.S.
Willmott J.R.
Hobbs M.J.
David J.P.R.
Tan C.H.
Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
description Lattice-mismatched InGaAs has appeared to be emerging semiconductor materials for sensors and photovoltaic applications. The absorption coefficients of the materials are crucial in designing high-performance semiconductor devices. Nevertheless, the absorption coefficient of lattice-mismatched InGaAs were not comprehensively studied to cater for the 2000�3000 nm applications. This study aims to determine the absorption coefficients of lattice-mismatched In0.73Ga0.27As and In0.83Ga0.17As semiconductor materials through photocurrent measurement which enables the absorption tail information to be extracted. In addition, this work demonstrates the incorporation of an innovative artificial intelligence-based method in solving the absorption coefficient of lattice-mismatched InGaAs, considering the detailed information of the structure design and material parameters. By selecting the best gene for the next iteration, the utilization of Genetic Algorithm has significantly reduced the number of iterations from a maximum of 10 000 to 300. Validation of the algorithm was conducted, showing a good agreement of absorption coefficient result compared to the published work on In0.72Ga0.28As. The absorption coefficient of In0.83Ga0.17As with an extended cutoff wavelength near 2.6 ?m is newly reported in this paper. In addition, the extrapolation of the obtained absorption results demonstrates energy gaps of 0.475 eV for In0.73Ga0.27As and 0.55 eV for In0.83Ga0.17As, which are compatible with the reported bandgaps of these materials. The extracted absorption coefficient information can be used in the design of semiconductor devices for emerging technologies such as focal plane array, short wave infrared sensing and thermophotovoltaic. � 2022 Elsevier Ltd
author2 57190622221
author_facet 57190622221
Lee H.J.
Ali Gamel M.M.
Ker P.J.
Jamaludin M.Z.
Wong Y.H.
Yap K.S.
Willmott J.R.
Hobbs M.J.
David J.P.R.
Tan C.H.
format Article
author Lee H.J.
Ali Gamel M.M.
Ker P.J.
Jamaludin M.Z.
Wong Y.H.
Yap K.S.
Willmott J.R.
Hobbs M.J.
David J.P.R.
Tan C.H.
author_sort Lee H.J.
title Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
title_short Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
title_full Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
title_fullStr Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
title_full_unstemmed Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm
title_sort deriving the absorption coefficients of lattice mismatched ingaas using genetic algorithm
publisher Elsevier Ltd
publishDate 2024
_version_ 1814061068351176704