Every cell needs a beautiful image: on-the-fly contacting measurements for high-throughput production

Abstract

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.

Deep Learning Model to Denoise Luminescence Images of Silicon Solar Cells

“…[37] Relevant to this study, Kurumundayil et al have successfully used a GAN to improve the image quality of blurred EL images [38] and to remove unwanted marks when analyzing silicon wafer images. [39] This paper investigates the efficacy of using U-net, a deep learning model, to reduce noise in both EL and PL images. Previous research in other fields has demonstrated that U-nets outperform other models, including GANs, in terms of its architectural simplicity and effectiveness in denoising.…”

“…The models that have shown high performance in denoising applications [ 24 ] include autoencoders, [ 34 ] generative adversarial networks (GANs), [ 35 ] and U‐nets [ 36 ] with GANs and U‐nets performing particularly well. [ 37 ] Relevant to this study, Kurumundayil et al have successfully used a GAN to improve the image quality of blurred EL images [ 38 ] and to remove unwanted marks when analyzing silicon wafer images. [ 39 ]…”

“…[ 37 ] Relevant to this study, Kurumundayil et al have successfully used a GAN to improve the image quality of blurred EL images [ 38 ] and to remove unwanted marks when analyzing silicon wafer images. [ 39 ]…”