https://doi.org/10.1051/epjpv/2022033
Regular Article
Every cell needs a beautiful image: on-the-fly contacting measurements for high-throughput production
1
Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstraße 2, 79110 Freiburg, Germany
2
halm Eletronik Gmbh, Friesstraße 20, 60388 Frankfurt am Main, Germany
* e-mail: leslie.lydia.kurumundayil@ise.fraunhofer.de
Received:
30
June
2022
Received in final form:
18
November
2022
Accepted:
13
December
2022
Published online: 7 February 2023
The future of the energy transition will lead to a terrawatt-scale photovoltaic market, which can be served cost-effectively primarily by means of high-throughput production of solar cells. In addition to high-throughput production, characterization must be adapted to highest cycle times. Therefore, we present an innovative approach to detect image defects in solar cells using on-the-fly electroluminescence measurements. When a solar cell passes a standard current–voltage (I–V) unit, the cell is stopped, contacted, measured, released, and afterwards again accelerated. In contrast to this, contacting and measuring the sample on-the-fly saves a lot of time. Yet, the resulting images are blurred due to high-speed motion. For the development of such an on-the-fly contact measurement tool, a deblurring method is developed in this work. Our deep-learning-based deblurring model enables to present a clean EL image of the solar cell to the human operator and allows for a proper defect detection, reaching a correlation coefficient of 0.84.
Key words: Photovoltaics / characterization / deep learning / generative adversarial networks / synthetic data
© L. Kurumundayil et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
