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

Xinyi Fan, Bojun Wang, Muhammad Quddamah Khokhar, Muhammad Aleem Zahid, Duy Phong Pham () and Junsin Yi ()
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Xinyi Fan: Interdisciplinary Program in Photovoltaic System Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Bojun Wang: College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea
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

Energies, 2023, vol. 16, issue 16, 1-13

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.

Keywords: deep learning; ITO; sputter; thickness; CNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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