A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics

Abstract We report a fast, reliable and non-destructive method for quantifying the homogeneity of perovskite thin films over large areas using machine vision. We adapt existing machine vision algorithms to spatially quantify multiple perovskite film properties (substrate coverage, film thickness, de...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nina Taherimakhsousi, Mathilde Fievez, Benjamin P. MacLeod, Edward P. Booker, Emmanuelle Fayard, Muriel Matheron, Matthieu Manceau, Stéphane Cros, Solenn Berson, Curtis P. Berlinguette
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/93667ca46ddd41e09df0a36ed00798d4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:93667ca46ddd41e09df0a36ed00798d4
record_format	dspace
spelling	oai:doaj.org-article:93667ca46ddd41e09df0a36ed00798d42021-11-28T12:13:46ZA machine vision tool for facilitating the optimization of large-area perovskite photovoltaics10.1038/s41524-021-00657-82057-3960https://doaj.org/article/93667ca46ddd41e09df0a36ed00798d42021-11-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00657-8https://doaj.org/toc/2057-3960Abstract We report a fast, reliable and non-destructive method for quantifying the homogeneity of perovskite thin films over large areas using machine vision. We adapt existing machine vision algorithms to spatially quantify multiple perovskite film properties (substrate coverage, film thickness, defect density) with pixel resolution from pictures of 25 cm2 samples. Our machine vision tool—called PerovskiteVision—can be combined with an optical model to predict photovoltaic cell and module current density from the perovskite film thickness. We use the measured film properties and predicted device current density to identify a posteriori the process conditions that simultaneously maximize the device performance and the manufacturing throughput for large-area perovskite deposition using gas-knife assisted slot-die coating. PerovskiteVision thus facilitates the transfer of a new deposition process to large-scale photovoltaic module manufacturing. This work shows how machine vision can accelerate slow characterization steps essential for the multi-objective optimization of thin film deposition processes.Nina TaherimakhsousiMathilde FievezBenjamin P. MacLeodEdward P. BookerEmmanuelle FayardMuriel MatheronMatthieu ManceauStéphane CrosSolenn BersonCurtis P. BerlinguetteNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Nina Taherimakhsousi Mathilde Fievez Benjamin P. MacLeod Edward P. Booker Emmanuelle Fayard Muriel Matheron Matthieu Manceau Stéphane Cros Solenn Berson Curtis P. Berlinguette A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
description	Abstract We report a fast, reliable and non-destructive method for quantifying the homogeneity of perovskite thin films over large areas using machine vision. We adapt existing machine vision algorithms to spatially quantify multiple perovskite film properties (substrate coverage, film thickness, defect density) with pixel resolution from pictures of 25 cm2 samples. Our machine vision tool—called PerovskiteVision—can be combined with an optical model to predict photovoltaic cell and module current density from the perovskite film thickness. We use the measured film properties and predicted device current density to identify a posteriori the process conditions that simultaneously maximize the device performance and the manufacturing throughput for large-area perovskite deposition using gas-knife assisted slot-die coating. PerovskiteVision thus facilitates the transfer of a new deposition process to large-scale photovoltaic module manufacturing. This work shows how machine vision can accelerate slow characterization steps essential for the multi-objective optimization of thin film deposition processes.
format	article
author	Nina Taherimakhsousi Mathilde Fievez Benjamin P. MacLeod Edward P. Booker Emmanuelle Fayard Muriel Matheron Matthieu Manceau Stéphane Cros Solenn Berson Curtis P. Berlinguette
author_facet	Nina Taherimakhsousi Mathilde Fievez Benjamin P. MacLeod Edward P. Booker Emmanuelle Fayard Muriel Matheron Matthieu Manceau Stéphane Cros Solenn Berson Curtis P. Berlinguette
author_sort	Nina Taherimakhsousi
title	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
title_short	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
title_full	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
title_fullStr	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
title_full_unstemmed	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
title_sort	machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/93667ca46ddd41e09df0a36ed00798d4
work_keys_str_mv	AT ninataherimakhsousi amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT mathildefievez amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT benjaminpmacleod amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT edwardpbooker amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT emmanuellefayard amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT murielmatheron amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT matthieumanceau amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT stephanecros amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT solennberson amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT curtispberlinguette amachinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT ninataherimakhsousi machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT mathildefievez machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT benjaminpmacleod machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT edwardpbooker machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT emmanuellefayard machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT murielmatheron machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT matthieumanceau machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT stephanecros machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT solennberson machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics AT curtispberlinguette machinevisiontoolforfacilitatingtheoptimizationoflargeareaperovskitephotovoltaics
_version_	1718408155530002432

A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics

Ejemplares similares