Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this technique. An analytical method was introduced to calculate optical co...

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Autores principales:	Shreeniket Joshi, Amirkianoosh Kiani
Formato:	article
Lenguaje:	EN
Publicado:	Institue of Optics and Electronics, Chinese Academy of Sciences 2021
Materias:	3d nanonetwork nanostructures optical properties artificial neural network. Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/6245394f395241d0a9cab1f2f2fb20c4
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spelling	oai:doaj.org-article:6245394f395241d0a9cab1f2f2fb20c42021-11-17T07:53:54ZHybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures2096-457910.29026/oea.2021.210039https://doaj.org/article/6245394f395241d0a9cab1f2f2fb20c42021-09-01T00:00:00Zhttp://www.oejournal.org/article/doi/10.29026/oea.2021.210039https://doaj.org/toc/2096-4579The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this technique. An analytical method was introduced to calculate optical constants from reflectance and transmittance spectra. Optical band gaps for this novel material and other important insights on the physical properties were derived from the optical constants. The existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available. A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum alone. The hybrid method introduced in this study was proved to be effective with an accuracy of 95%.Shreeniket JoshiAmirkianoosh KianiInstitue of Optics and Electronics, Chinese Academy of Sciencesarticle3d nanonetworknanostructuresoptical propertiesartificial neural network.Optics. LightQC350-467ENOpto-Electronic Advances, Vol 4, Iss 10, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	3d nanonetwork nanostructures optical properties artificial neural network. Optics. Light QC350-467
spellingShingle	3d nanonetwork nanostructures optical properties artificial neural network. Optics. Light QC350-467 Shreeniket Joshi Amirkianoosh Kiani Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
description	The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this technique. An analytical method was introduced to calculate optical constants from reflectance and transmittance spectra. Optical band gaps for this novel material and other important insights on the physical properties were derived from the optical constants. The existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available. A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum alone. The hybrid method introduced in this study was proved to be effective with an accuracy of 95%.
format	article
author	Shreeniket Joshi Amirkianoosh Kiani
author_facet	Shreeniket Joshi Amirkianoosh Kiani
author_sort	Shreeniket Joshi
title	Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
title_short	Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
title_full	Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
title_fullStr	Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
title_full_unstemmed	Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
title_sort	hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3d nanonetwork silicon structures
publisher	Institue of Optics and Electronics, Chinese Academy of Sciences
publishDate	2021
url	https://doaj.org/article/6245394f395241d0a9cab1f2f2fb20c4
work_keys_str_mv	AT shreeniketjoshi hybridartificialneuralnetworksandanalyticalmodelforpredictionofopticalconstantsandbandgapenergyof3dnanonetworksiliconstructures AT amirkianooshkiani hybridartificialneuralnetworksandanalyticalmodelforpredictionofopticalconstantsandbandgapenergyof3dnanonetworksiliconstructures
_version_	1718425862367346688

Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

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