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3-D Modeling of Multicrystalline Silicon Materials and Solar Cells

: Sio, Hang Cheong; Fell, Andreas; Phang, Sieu Pheng; Wang, Haitao; Zheng, Peiting; Chen, D.K.; Zhang, Xinyu; Zhang, Tao; Jin, Hao; Macdonald, Daniel

Postprint urn:nbn:de:0011-n-5557732 (4.1 MByte PDF)
MD5 Fingerprint: 5c39109c9e6a9cea032a02006cac5562
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Erstellt am: 5.9.2019

IEEE Journal of Photovoltaics 9 (2019), Nr.4, S.965-973
ISSN: 2156-3381
ISSN: 2156-3403
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01DR17019; CCPV
Collaboration Cluster for Photovoltaic Silicon Material Characterization
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ISE ()
grain boundary; semiconductor impurities; photoluminescence; semiconductor device modeling; silicon; Photovoltaik; Silicium-Photovoltaik; Herstellung und Analyse von hocheffizienten Solarzellen

We present a method to model large-area multicrystalline materials using Quokka3, based on artificially generated lifetime images created by combining injection-dependent intragrain lifetimes and defect recombination velocity maps extracted from photoluminescence-based lifetime images. It is found that simulations based only on measured lifetime images underestimate the detrimental impacts of crystal defects and, hence, overestimate the overall cell performance of multicrystalline silicon solar cells. As demonstration, we applied Quokka3 simulations to resolve various bulk recombination losses in industrial multicrystalline silicon solar cells, quantify the effects of phosphorus diffusion and hydrogenation on the final cell performance, and evaluate the detrimental impact of crystal defects on individual cell parameters. Through numerical simulations, this paper has also demonstrated the potential errors of using a single average value to predict the cell performance of inhomogeneous multicrystalline silicon materials. It is observed that the commonly used harmonic mean can provide a good indication for V oc , but is less effective for predicting J sc , and is almost entirely ineffective for predicting the fill factor.