A deep-learning approach for rapid prediction of spectral responses of meta-atoms

Zhenxiang Shi; Haiou Lu; Xinyu Yu; Kai Ni; Qian Zhou; Xiaohao Wang

doi:10.1117/12.2687096

27 November 2023 A deep-learning approach for rapid prediction of spectral responses of meta-atoms

Zhenxiang Shi, Haiou Lu, Xinyu Yu, Kai Ni, Qian Zhou, Xiaohao Wang

Proceedings Volume 12769, Optical Metrology and Inspection for Industrial Applications X; 127690M (2023) https://doi.org/10.1117/12.2687096
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Abstract

In traditional metasurface structure design, it heavily relies on electromagnetic simulations to obtain transmission and phase spectral, followed by empirical adjustments. This iterative trial-and-error process, especially when dealing with multi-objective optimization tasks, demands intensive and time-consuming computations, which to a certain extent restricts the development of the metasurface research field. In this paper, a proposed method achieves rapid prediction of spectral responses corresponding to structural units by seeking analytical solutions within the constructed neural network model. The proposed deep learning-based method for predicting transmission and phase spectral of metasurface units consists of metasurface unit dataset construction and a ResNet-based network framework. In the dataset construction approach, an overhead view of the unit structure is extracted and transformed into a binary image, where scaling factors are coupled into the two-dimensional image to increase dimensionality. This enables the representation of different structures such as square pillars, elliptical cylinders, and varying sizes of metasurface units using the same data format, significantly enhancing network generalization. Within the network framework, ResNet is employed to predict the real and imaginary parts of the S21 parameter, which are then inverted to obtain transmission and phase information. The progressive training method employed in combination with this framework yields high prediction accuracy. The deep learning-based method for predicting transmission and phase spectral of dielectric metasurface units, as revealed in this paper, achieves a 7200-fold increase in prediction speed compared to traditional electromagnetic

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenxiang Shi, Haiou Lu, Xinyu Yu, Kai Ni, Qian Zhou, and Xiaohao Wang "A deep-learning approach for rapid prediction of spectral responses of meta-atoms", Proc. SPIE 12769, Optical Metrology and Inspection for Industrial Applications X, 127690M (27 November 2023); https://doi.org/10.1117/12.2687096

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KEYWORDS

Design and modelling

Education and training

Electromagnetic simulation

Finite-difference time-domain method

Infrared radiation

Infrared sensors

Light absorption

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