No Cover Image

Journal article 7 views

An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films

Juan Pablo Velásquez, Juan José Patiño, Keony Jimenez, Santiago Mesa, Milton Perez, Ershad Parvazian, Edwin Alexander Ramírez, Rafael Betancur, Trystan Watson Orcid Logo, Franklin Jaramillo Orcid Logo

Advanced Electronic Materials, Volume: 12, Issue: 11, Start page: e00877

Swansea University Authors: Ershad Parvazian, Trystan Watson Orcid Logo

  • 72153.VOR.pdf

    PDF | Version of Record

    © 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.

    Download (2.16MB)

Check full text

DOI (Published version): 10.1002/aelm.202500877

Abstract

Perovskite technology offers an advantage over conventional photovoltaics technologies related to its capacity to be manufactured on flexible substrates using continuous printing techniques, delivering an improved cost–benefit ratio. However, producing perovskite solar cells (PSCs) via these approac...

Full description

Published in: Advanced Electronic Materials
ISSN: 2199-160X 2199-160X
Published: Wiley 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa72153
first_indexed 2026-06-24T10:22:49Z
last_indexed 2026-06-24T10:22:49Z
id cronfa72153
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>72153</id><entry>2026-06-24</entry><title>An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films</title><swanseaauthors><author><sid>59dc6f18dde94e2a5fb2edd858270ec3</sid><ORCID/><firstname>Ershad</firstname><surname>Parvazian</surname><name>Ershad Parvazian</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a210327b52472cfe8df9b8108d661457</sid><ORCID>0000-0002-8015-1436</ORCID><firstname>Trystan</firstname><surname>Watson</surname><name>Trystan Watson</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-06-24</date><deptcode>EAAS</deptcode><abstract>Perovskite technology offers an advantage over conventional photovoltaics technologies related to its capacity to be manufactured on flexible substrates using continuous printing techniques, delivering an improved cost–benefit ratio. However, producing perovskite solar cells (PSCs) via these approaches demands rigorous quality control of printed films, as film uniformity influences device performance. This quality control process often involves characterization techniques, such as profilometry that is destructive, making them unsuitable for in‐line real‐time or large‐scale inspections. Here, we present a versatile in‑line inspection framework that combines a low‑cost webcam‑based transmitted‑light imaging setup with a deep‑learning regression model to estimate the thickness of printed MAPbI 3 and FAPbI 3 films in real time. The resulting prototype achieves coefficients of determination (R 2 ) of 0.985 and 0.986 and root‑mean‑square errors of 18 and 25 nm, respectively which is comparable to conventional invasive profilometry. When applied to 25 cm 2 flexible mini‑modules, an inverse correlation between layer thickness variability and power conversion efficiency (PCE) was observed, and integrating the prototype into in‐line inspection opened new possibilities for data‐driven decision‐making. This in‐line, high‐spatial‐resolution inspection framework enables the fully automated and high‐throughput manufacturing of large‐area perovskite solar modules, by allowing real‐time optimization of key operational parameters.</abstract><type>Journal Article</type><journal>Advanced Electronic Materials</journal><volume>12</volume><journalNumber>11</journalNumber><paginationStart>e00877</paginationStart><paginationEnd/><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2199-160X</issnPrint><issnElectronic>2199-160X</issnElectronic><keywords>deep learning, In-line characterization, machine learning, machine vision, perovskite, roll-to-roll manufacturing, thickness inspection</keywords><publishedDay>8</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-06-08</publishedDate><doi>10.1002/aelm.202500877</doi><url/><notes/><college>COLLEGE NANME</college><department>Engineering and Applied Sciences School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EAAS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>University of Antioquia; Colombian Government through the SGR project BPIN. Grant Number: 2022000100012; EPSRC Centre for Doctoral Training in Technology Enhanced Chemical Synthesis. Grant Number: EP/X025217/1.</funders><projectreference/><lastEdited>2026-06-24T11:24:11.7747602</lastEdited><Created>2026-06-24T11:15:58.7741918</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Materials Science and Engineering</level></path><authors><author><firstname>Juan Pablo</firstname><surname>Velásquez</surname><order>1</order></author><author><firstname>Juan José</firstname><surname>Patiño</surname><order>2</order></author><author><firstname>Keony</firstname><surname>Jimenez</surname><order>3</order></author><author><firstname>Santiago</firstname><surname>Mesa</surname><order>4</order></author><author><firstname>Milton</firstname><surname>Perez</surname><order>5</order></author><author><firstname>Ershad</firstname><surname>Parvazian</surname><orcid/><order>6</order></author><author><firstname>Edwin Alexander</firstname><surname>Ramírez</surname><order>7</order></author><author><firstname>Rafael</firstname><surname>Betancur</surname><order>8</order></author><author><firstname>Trystan</firstname><surname>Watson</surname><orcid>0000-0002-8015-1436</orcid><order>9</order></author><author><firstname>Franklin</firstname><surname>Jaramillo</surname><orcid>0000-0003-1722-5487</orcid><order>10</order></author></authors><documents><document><filename>72153__37034__6ad0e85b00ff49548b13c2089ef0c0ae.pdf</filename><originalFilename>72153.VOR.pdf</originalFilename><uploaded>2026-06-24T11:21:18.5613575</uploaded><type>Output</type><contentLength>2264900</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 72153 2026-06-24 An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films 59dc6f18dde94e2a5fb2edd858270ec3 Ershad Parvazian Ershad Parvazian true false a210327b52472cfe8df9b8108d661457 0000-0002-8015-1436 Trystan Watson Trystan Watson true false 2026-06-24 EAAS Perovskite technology offers an advantage over conventional photovoltaics technologies related to its capacity to be manufactured on flexible substrates using continuous printing techniques, delivering an improved cost–benefit ratio. However, producing perovskite solar cells (PSCs) via these approaches demands rigorous quality control of printed films, as film uniformity influences device performance. This quality control process often involves characterization techniques, such as profilometry that is destructive, making them unsuitable for in‐line real‐time or large‐scale inspections. Here, we present a versatile in‑line inspection framework that combines a low‑cost webcam‑based transmitted‑light imaging setup with a deep‑learning regression model to estimate the thickness of printed MAPbI 3 and FAPbI 3 films in real time. The resulting prototype achieves coefficients of determination (R 2 ) of 0.985 and 0.986 and root‑mean‑square errors of 18 and 25 nm, respectively which is comparable to conventional invasive profilometry. When applied to 25 cm 2 flexible mini‑modules, an inverse correlation between layer thickness variability and power conversion efficiency (PCE) was observed, and integrating the prototype into in‐line inspection opened new possibilities for data‐driven decision‐making. This in‐line, high‐spatial‐resolution inspection framework enables the fully automated and high‐throughput manufacturing of large‐area perovskite solar modules, by allowing real‐time optimization of key operational parameters. Journal Article Advanced Electronic Materials 12 11 e00877 Wiley 2199-160X 2199-160X deep learning, In-line characterization, machine learning, machine vision, perovskite, roll-to-roll manufacturing, thickness inspection 8 6 2026 2026-06-08 10.1002/aelm.202500877 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Another institution paid the OA fee University of Antioquia; Colombian Government through the SGR project BPIN. Grant Number: 2022000100012; EPSRC Centre for Doctoral Training in Technology Enhanced Chemical Synthesis. Grant Number: EP/X025217/1. 2026-06-24T11:24:11.7747602 2026-06-24T11:15:58.7741918 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Juan Pablo Velásquez 1 Juan José Patiño 2 Keony Jimenez 3 Santiago Mesa 4 Milton Perez 5 Ershad Parvazian 6 Edwin Alexander Ramírez 7 Rafael Betancur 8 Trystan Watson 0000-0002-8015-1436 9 Franklin Jaramillo 0000-0003-1722-5487 10 72153__37034__6ad0e85b00ff49548b13c2089ef0c0ae.pdf 72153.VOR.pdf 2026-06-24T11:21:18.5613575 Output 2264900 application/pdf Version of Record true © 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
spellingShingle An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
Ershad Parvazian
Trystan Watson
title_short An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
title_full An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
title_fullStr An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
title_full_unstemmed An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
title_sort An In‐Line Machine Vision–Based Profilometry Tool for Non‐Destructive Thickness Assessment of Perovskite Films
author_id_str_mv 59dc6f18dde94e2a5fb2edd858270ec3
a210327b52472cfe8df9b8108d661457
author_id_fullname_str_mv 59dc6f18dde94e2a5fb2edd858270ec3_***_Ershad Parvazian
a210327b52472cfe8df9b8108d661457_***_Trystan Watson
author Ershad Parvazian
Trystan Watson
author2 Juan Pablo Velásquez
Juan José Patiño
Keony Jimenez
Santiago Mesa
Milton Perez
Ershad Parvazian
Edwin Alexander Ramírez
Rafael Betancur
Trystan Watson
Franklin Jaramillo
format Journal article
container_title Advanced Electronic Materials
container_volume 12
container_issue 11
container_start_page e00877
publishDate 2026
institution Swansea University
issn 2199-160X
2199-160X
doi_str_mv 10.1002/aelm.202500877
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description Perovskite technology offers an advantage over conventional photovoltaics technologies related to its capacity to be manufactured on flexible substrates using continuous printing techniques, delivering an improved cost–benefit ratio. However, producing perovskite solar cells (PSCs) via these approaches demands rigorous quality control of printed films, as film uniformity influences device performance. This quality control process often involves characterization techniques, such as profilometry that is destructive, making them unsuitable for in‐line real‐time or large‐scale inspections. Here, we present a versatile in‑line inspection framework that combines a low‑cost webcam‑based transmitted‑light imaging setup with a deep‑learning regression model to estimate the thickness of printed MAPbI 3 and FAPbI 3 films in real time. The resulting prototype achieves coefficients of determination (R 2 ) of 0.985 and 0.986 and root‑mean‑square errors of 18 and 25 nm, respectively which is comparable to conventional invasive profilometry. When applied to 25 cm 2 flexible mini‑modules, an inverse correlation between layer thickness variability and power conversion efficiency (PCE) was observed, and integrating the prototype into in‐line inspection opened new possibilities for data‐driven decision‐making. This in‐line, high‐spatial‐resolution inspection framework enables the fully automated and high‐throughput manufacturing of large‐area perovskite solar modules, by allowing real‐time optimization of key operational parameters.
published_date 2026-06-08T11:24:13Z
_version_ 1868873495927062528
score 11.11042

Similar Items