CC BY 4.0Kurumundayil, Leslie LydiaLeslie LydiaKurumundayilRamspeck, KlausKlausRamspeckRein, StefanStefanReinDemant, MatthiasMatthiasDemant2023-03-132023-03-132023Note-ID: 0000946Ehttps://publica.fraunhofer.de/handle/publica/437580https://doi.org/10.24406/publica-103210.1051/epjpv/202203310.24406/publica-1032The 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 highthroughput 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.enPhotovoltaicscharacterizationdeep learninggenerative adversarial networkssynthetic dataPhotovoltaikSilicium-PhotovoltaikCharakterisierung von Prozess- und Silicium-MaterialienTechnologie- und Nachhaltigkeitsbewertungjournal article