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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.
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]
Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning.
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  Data: <searchLink fieldCode="AR" term="%22Chang%2C+Yi-Hsun%22">Chang, Yi-Hsun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+You-Lun%22">Zhang, You-Lun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cheng%2C+Cheng-Hao%22">Cheng, Cheng-Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Shu-Han%22">Wu, Shu-Han</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Cheng-Han%22">Li, Cheng-Han</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Su-Yu%22">Liao, Su-Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tseng%2C+Zi-Chun%22">Tseng, Zi-Chun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Ming-Yi%22">Lin, Ming-Yi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Chun-Ying%22">Huang, Chun-Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Jul2025, Vol. 15 Issue 14, p1112. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+cells%22">Solar cells</searchLink><br /><searchLink fieldCode="DE" term="%22Photovoltaic+power+generation%22">Photovoltaic power generation</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+analysis%22">Materials analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/nano15141112
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        Text: English
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Solar cells
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      – SubjectFull: Photovoltaic power generation
        Type: general
      – SubjectFull: Classification
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      – SubjectFull: Multilayer perceptrons
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      – SubjectFull: Materials analysis
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      – SubjectFull: Manufacturing process automation
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      – TitleFull: Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning.
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              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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