How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/138484 |
| _version_ | 1826203897566330880 |
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| author | Kurchin, Rachel C Poindexter, Jeremy R Vahanissi, Ville Savin, Hele del Canizo, Carlos Buonassisi, Tonio |
| author_facet | Kurchin, Rachel C Poindexter, Jeremy R Vahanissi, Ville Savin, Hele del Canizo, Carlos Buonassisi, Tonio |
| author_sort | Kurchin, Rachel C |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:45:05Z |
| format | Article |
| id | mit-1721.1/138484 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:45:05Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1384842021-12-15T03:20:14Z How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements Kurchin, Rachel C Poindexter, Jeremy R Vahanissi, Ville Savin, Hele del Canizo, Carlos Buonassisi, Tonio 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. 2021-12-14T19:58:13Z 2021-12-14T19:58:13Z 2020 2021-12-14T19:54:18Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138484 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). en 10.1109/JPHOTOV.2020.3010105 IEEE Journal of Photovoltaics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Kurchin, Rachel C Poindexter, Jeremy R Vahanissi, Ville Savin, Hele del Canizo, Carlos Buonassisi, Tonio How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title | How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title_full | How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title_fullStr | How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title_full_unstemmed | How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title_short | How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements |
| title_sort | how much physics is in a current voltage curve inferring defect properties from photovoltaic device measurements |
| url | https://hdl.handle.net/1721.1/138484 |
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