Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning.
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| Title: | Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning. |
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| Authors: | Chang, Yi-Hsun1 (AUTHOR), Zhang, You-Lun1,2 (AUTHOR), Cheng, Cheng-Hao1,2 (AUTHOR), Wu, Shu-Han2 (AUTHOR), Li, Cheng-Han2 (AUTHOR), Liao, Su-Yu1 (AUTHOR), Tseng, Zi-Chun1 (AUTHOR), Lin, Ming-Yi2 (AUTHOR), Huang, Chun-Ying1 (AUTHOR) |
| Source: | Nanomaterials (2079-4991). Jul2025, Vol. 15 Issue 14, p1112. 13p. |
| Subjects: | Deep learning, Solar cells, Photovoltaic power generation, Classification, Multilayer perceptrons, Materials analysis, Manufacturing process automation |
| Abstract: | Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20794991 |
| DOI: | 10.3390/nano15141112 |
