A Numerical-Optimizing Method of Metasurface Inverse Design Based on Method of Moment and Particle Swarm Optimization for the Scattering Field Modulation

Yi Yuan,Jun Ding,Chao Huang,Chenjiang Guo,Xia Ma,Xun Qu,Qi Zheng

Applied Physics A（2024）

Northwestern Polytechnical University | Shanghai University

Cited 0|Views2

Abstract

In this paper, an efficient numerical-optimizing hybrid method for manipulating the scattering field of electromagnetic metasurfaces was proposed. Firstly, a mathematical model of a metasurface based on surface impedance was established. The surface impedance was calculated using the method of moments in numerical. Secondly, an improved particle swarm optimization algorithm was proposed to optimize the surface impedance so that the real part of the surface impedance is zero. Finally, to realize the optimized surface impedance physically, classical capacitive structures were considered and verified in full-wave simulation. It was demonstrated that the scattering field generated by the physical structure closely matched the desired field, which demonstrated the effectiveness and accuracy of the proposed method. The proposed method provides a systematic and efficient method for metasurfaces design, enabling precise modulation of scattering fields. The method proposed in this paper has a great time advantage compared with the conventional design methods of metasurface.

Translated text

Key words

Anomalous metasurface,Perfect reflection metasurface,Inverse design,Method of moment,Particle swarm optimization

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper