Prof. Qiong Wang Profile

Prof. Qiong Wang

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Proceedings Article | 28 March 2024 Paper

Predicting electromagnetic response of electromagnetic metasurface using deep learning

Bingyan Yang, Qiong Wang, Lei Mu

Proceedings Volume 13091, 130912I (2024) https://doi.org/10.1117/12.3023137

KEYWORDS: Electromagnetism, Education and training, Neural networks, Design, Deep learning, Machine learning, Deep convolutional neural networks, Data modeling, Matrices, Binary data

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Digitally coded metasurface (DMS) is a novel class of electromagnetic materials that possess the ability to manipulate electromagnetic waves at scales significantly smaller than the wavelength. They hold great potential for a wide range of applications, including wireless communication, millimeter-wave imaging, data storage, and sensing. However, the conventional full-wave simulation methods currently employed often require precise geometric modeling, grid planning, and consideration of complex physical models such as material parameters and boundary conditions. These time-consuming and costly approaches face significant challenges in large-scale electromagnetic response studies. Therefore, there is an urgent need to explore more efficient and cost-effective methods to address these issues. In this paper, we propose a deep learning-based approach for predicting the electromagnetic response of individual units in a DMS. We construct a convolutional neural network (CNN) model that can accurately and real-time predict the electromagnetic response based on the input coding pattern. To train and validate the model, we utilize a substantial amount of electromagnetic simulation data and incorporate amplitude constraints into the loss function for model optimization. A way to abandon the dual network structure and achieve simultaneous prediction of dual parameters with smaller computational requirements. Experimental results demonstrate that our model achieves highly accurate predictions of both amplitude and phase responses of DMS, surpassing traditional numerical methods in terms of efficiency and scalability. The proposed deep learning approach offers a promising solution for efficient and low-cost prediction of electromagnetic responses in DMS.

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