Abstract
Thin- film photovoltaics (PV), and in particular Copper Indium Gallium Selenide (CIGS) technologies, will play an important role in the turnaround in inevitable energy policy due to their high efficiencies, easy installation, high product flexibility, and a lower carbon footprint when compared to silicon solar cells. However, due to the delicate processing and associated costs in the manufacturing of CIGS cells, inline quality control during production is a hot topic for PV industry. In this work we demonstrate that photoluminescence (PL) imaging can be a powerful enabling technology for improving the process efficiency. Using Python based image processing and analysis, defects that lead to failure of individual cells can be detected early in the production process, which ultimately saves resources and costs by not further processing nonconformal batches.
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