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Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks

Author

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  • Giacomo Capizzi

    (Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
    Institute of Mathematics, Silesian University of Technology, Kaszubska, 23, 44-100 Gliwice, Poland)

  • Grazia Lo Sciuto

    (Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy)

  • Christian Napoli

    (Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy)

  • Rafi Shikler

    (Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O.B. 653 Beer-Sheva, Israel)

  • Marcin Woźniak

    (Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
    Institute of Mathematics, Silesian University of Technology, Kaszubska, 23, 44-100 Gliwice, Poland)

Abstract

In this paper we devise a neural-network-based model to improve the production workflow of organic solar cells (OSCs). The investigated neural model is used to reckon the relation between the OSC’s generated power and several device’s properties such as the geometrical parameters and the active layers thicknesses. Such measurements were collected during an experimental campaign conducted on 80 devices. The collected data suggest that the maximum generated power depends on the active layer thickness. The mathematical model of such a relation has been determined by using a feedforward neural network (FFNN) architecture as a universal function approximator. The performed simulations show good agreement between simulated and experimental data with an overall error of about 9%. The obtained results demonstrate that the use of a neural model can be useful to improve the OSC manufacturing processes.

Suggested Citation

  • Giacomo Capizzi & Grazia Lo Sciuto & Christian Napoli & Rafi Shikler & Marcin Woźniak, 2018. "Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks," Energies, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1221-:d:145622
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    References listed on IDEAS

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    1. Kang-Shyang Liao & Soniya D. Yambem & Amrita Haldar & Nigel J. Alley & Seamus A. Curran, 2010. "Designs and Architectures for the Next Generation of Organic Solar Cells," Energies, MDPI, vol. 3(6), pages 1-39, June.
    2. Rafique, Saqib & Abdullah, Shahino Mah & Sulaiman, Khaulah & Iwamoto, Mitsumasa, 2018. "Fundamentals of bulk heterojunction organic solar cells: An overview of stability/degradation issues and strategies for improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 43-53.
    3. Mine Kaya & Shima Hajimirza, 2017. "Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization," Energies, MDPI, vol. 10(12), pages 1-11, November.
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    Cited by:

    1. Cui, Zhiquan & Yan, Zhiqi & Zhao, Minghang & Zhong, Shisheng, 2022. "Gas path parameter prediction of aero-engine based on an autoregressive discrete convolution sum process neural network," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).

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