Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning
Abstract A solar cell's internal quantum efficiency (IQE) measurement reveals critical information about the device's performance. This information can be obtained using a qualitative analysis of the shape of the curve, identifying and attributing current losses such as at the front and re...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202407048 |
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| author | Zubair Abdullah‐Vetter Brendan Wright Tien‐Chun Wu Ali Shakiba Ziv Hameiri |
| author_facet | Zubair Abdullah‐Vetter Brendan Wright Tien‐Chun Wu Ali Shakiba Ziv Hameiri |
| author_sort | Zubair Abdullah‐Vetter |
| collection | DOAJ |
| description | Abstract A solar cell's internal quantum efficiency (IQE) measurement reveals critical information about the device's performance. This information can be obtained using a qualitative analysis of the shape of the curve, identifying and attributing current losses such as at the front and rear interfaces, and extracting key electrical and optical performance parameters. However, conventional methods to extract the performance parameters from IQE measurements are often time‐consuming and require manual fitting approaches. While several methodologies exist to extract those parameters from silicon solar cells, there is a lack of accessible approaches for non‐silicon cell technologies, like gallium arsenide cells, typically limiting the analysis to only the qualitative level. Therefore, this study proposes using a deep learning method to automatically predict multiple key parameters from IQE measurements of gallium arsenide cells. The proposed method is demonstrated to achieve a very high level of prediction accuracy across the entire range of parameter values and exhibits a high resilience for noisy measurements. By enhancing the quantitative analysis of IQE measurements, the method will unlock the full potential of quantum efficiency measurements as a powerful characterization tool for diverse solar cell technologies. |
| format | Article |
| id | doaj-art-d29cd91810f04523a932546463d7dbd6 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-d29cd91810f04523a932546463d7dbd62025-01-29T09:50:19ZengWileyAdvanced Science2198-38442025-01-01124n/an/a10.1002/advs.202407048Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep LearningZubair Abdullah‐Vetter0Brendan Wright1Tien‐Chun Wu2Ali Shakiba3Ziv Hameiri4University of New South Wales (UNSW) Sydney NSW 2052 AustraliaUniversity of New South Wales (UNSW) Sydney NSW 2052 AustraliaUniversity of New South Wales (UNSW) Sydney NSW 2052 AustraliaUniversity of New South Wales (UNSW) Sydney NSW 2052 AustraliaUniversity of New South Wales (UNSW) Sydney NSW 2052 AustraliaAbstract A solar cell's internal quantum efficiency (IQE) measurement reveals critical information about the device's performance. This information can be obtained using a qualitative analysis of the shape of the curve, identifying and attributing current losses such as at the front and rear interfaces, and extracting key electrical and optical performance parameters. However, conventional methods to extract the performance parameters from IQE measurements are often time‐consuming and require manual fitting approaches. While several methodologies exist to extract those parameters from silicon solar cells, there is a lack of accessible approaches for non‐silicon cell technologies, like gallium arsenide cells, typically limiting the analysis to only the qualitative level. Therefore, this study proposes using a deep learning method to automatically predict multiple key parameters from IQE measurements of gallium arsenide cells. The proposed method is demonstrated to achieve a very high level of prediction accuracy across the entire range of parameter values and exhibits a high resilience for noisy measurements. By enhancing the quantitative analysis of IQE measurements, the method will unlock the full potential of quantum efficiency measurements as a powerful characterization tool for diverse solar cell technologies.https://doi.org/10.1002/advs.202407048convolutional neural networkgallium arsenidenoise resiliencesolar |
| spellingShingle | Zubair Abdullah‐Vetter Brendan Wright Tien‐Chun Wu Ali Shakiba Ziv Hameiri Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning Advanced Science convolutional neural network gallium arsenide noise resilience solar |
| title | Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning |
| title_full | Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning |
| title_fullStr | Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning |
| title_full_unstemmed | Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning |
| title_short | Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning |
| title_sort | automatic quantitative analysis of internal quantum efficiency measurements of gaas solar cells using deep learning |
| topic | convolutional neural network gallium arsenide noise resilience solar |
| url | https://doi.org/10.1002/advs.202407048 |
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