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| Naji, M. |
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| Motta, Antonella |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Ali, M. A. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Matheron, Muriel
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Publications (5/5 displayed)
- 2021Impact of interfaces on perovskite solar cell’s stability
- 2021A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
- 2014Toluene-organic thin films partition coefficients analyzed with Langmuir adsorption theory and finite elements simulationscitations
- 2011CO 2 measurement using an AlN/SI SAW sensorcitations
- 2005Adsorption and Desorption Isotherms at Ambient Temperature Obtained by Ellipsometric Porosimetry to Probe Micropores in Ordered Mesoporous Silica Films
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article
A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics
Abstract
e 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 cm 2 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.