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Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

Author

Listed:
  • Xinyi Fan

    (Interdisciplinary Program in Photovoltaic System Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
    The authors contributed equally to this work.)

  • Bojun Wang

    (College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea
    The authors contributed equally to this work.)

  • Muhammad Quddamah Khokhar

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Muhammad Aleem Zahid

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Duy Phong Pham

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Junsin Yi

    (College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

The thickness of the indium tin oxide (ITO) layer is a critical parameter affecting the performance of solar cells. Traditional measurement methods require sample collection, leading to manufacturing interruptions and potential quality issues. In this paper, we propose a real-time, non-contact approach using deep learning and optical interference phenomena to estimate the thickness of ITO layers in solar cells. We develop a convolutional neural network (CNN) model that processes microscopic images of solar cells and predicts the ITO layer thickness. In addition, mean absolute error (MAE) and mean squared error (MSE) loss functions are combined to train the model. Experimental results demonstrate the effectiveness of our approach in accurately estimating the ITO layer thickness. The integration of computer vision and deep learning techniques provides a valuable tool for non-destructive testing and quality control in the manufacturing of solar cells. The loss of the model after training is reduced to 0.83, and the slope of the test value in the scatter plot with the true value of the ellipsometer is approximately equal to 1, indicating the high reliability of the model.

Suggested Citation

  • Xinyi Fan & Bojun Wang & Muhammad Quddamah Khokhar & Muhammad Aleem Zahid & Duy Phong Pham & Junsin Yi, 2023. "Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena," Energies, MDPI, vol. 16(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6049-:d:1219960
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    References listed on IDEAS

    as
    1. Inès Massiot & Andrea Cattoni & Stéphane Collin, 2020. "Progress and prospects for ultrathin solar cells," Nature Energy, Nature, vol. 5(12), pages 959-972, December.
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