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

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 appro...

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Bibliographic Details
Published in:Energies (Basel) 2023-08, Vol.16 (16), p.6049
Main Authors: Fan, Xinyi, Wang, Bojun, Khokhar, Muhammad Quddamah, Zahid, Muhammad Aleem, Pham, Duy Phong, Yi, Junsin
Format: Article
Language:English
Subjects:
Algorithms
Archaeology
Automation
CNN
Crack initiation
Datasets
Deep learning
Indium
ITO
Light
Machine learning
Machine vision
Manufacturing
Microscopy
Neural networks
Phase transitions
Photovoltaic cells
Preventive maintenance
Solar batteries
Solar cells
sputter
thickness
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16166049