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

IF 1.9 Q3 PHYSICS, APPLIED
Leslie Kurumundayil, K. Ramspeck, S. Rein, M. Demant
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引用次数: 1

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.
每个细胞都需要一个美丽的图像:高通量生产的实时接触测量
能源转型的未来将导致一个太瓦规模的光伏市场,这可以主要通过高通量生产太阳能电池来经济有效地服务。除了高通量生产,表征必须适应最高的周期时间。因此,我们提出了一种创新的方法来检测图像缺陷的太阳能电池使用即时电致发光测量。当太阳能电池通过一个标准的电流-电压(I-V)单位时,电池被停止、接触、测量、释放,然后再次加速。与此相反,即时接触和测量样品节省了大量时间。然而,由于高速运动,产生的图像是模糊的。为了开发这种实时接触测量工具,本工作开发了一种去模糊方法。我们基于深度学习的去模糊模型能够向人类操作员呈现太阳能电池的清晰EL图像,并允许适当的缺陷检测,相关系数达到0.84。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Photovoltaics
EPJ Photovoltaics PHYSICS, APPLIED-
CiteScore
2.30
自引率
4.00%
发文量
15
审稿时长
8 weeks
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