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How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements
Name
2020_Kurchin_IEEEJ.Photovoltaics.pdf
Description
Accepted version
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
fc6e1ebd83ad1a9c139492d413fbaae0
Author(s)
Kurchin, Rachel C
Poindexter, Jeremy R
Vahanissi, Ville
Savin, Hele
del Canizo, Carlos
Buonassisi, Tonio
Date Issued
2020
Journal
IEEE Journal of Photovoltaics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kurchin, Rachel C, Poindexter, Jeremy R, Vahanissi, Ville, Savin, Hele, del Canizo, Carlos et al. 2020. "How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements." IEEE Journal of Photovoltaics, 10 (6).
Version
Author's final manuscript
Abstract
Defect-assisted recombination processes are critical to understand, as they frequently limit the photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters alongside high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/JPHOTOV.2020.3010105
