Issue |
EPJ Appl. Metamat.
Volume 8, 2021
Metamaterials Research and Development in China
|
|
---|---|---|
Article Number | 15 | |
Number of page(s) | 4 | |
DOI | https://doi.org/10.1051/epjam/2021006 | |
Published online | 11 June 2021 |
https://doi.org/10.1051/epjam/2021006
Research Article
Tailoring the scattering properties of coding metamaterials based on machine learning
School of Electronic & Information Engineering, Harbin Institute of Technology, Harbin, PR China
* e-mail: qwu@hit.edu.cn
Received:
18
April
2021
Accepted:
18
April
2021
Published online: 11 June 2021
Diverse electromagnetic (EM) responses of coding metamaterials have been investigated, and the general research method is to use full-wave simulation. But if we only care its scattering properties, it is not necessary to perform full-wave simulation, which is usually time-consuming. Machine learning has significantly impelled the development of automatic design and optimize coding matrix. Based on metamaterial particle that has multiple response and genetic algorithm which is coupled with the scattering pattern analysis, we can optimize the coding matrix quickly to tailor the scattering properties without conducting full-wave simulation a lot of times for optimization. Since the coding matrix control of each particle allow modulation of EM wave, various EM phenomena can be achieved easier. In this paper, we proposed two reflective unitcells with different reflection phase, and then a semi-analytical model is built up for unitcells. To tailor the scattering properties, genetic algorithm normally based on binary coding, is coupled with the scattering pattern analysis in order to optimize the coding matrix. Finally, simulation results are compared with the semi-analytical calculation results and it is found that the simulation results agree very well with the theoretical values.
Key words: Machine learning / coding metamaterials / optimization of coding matrix
© S. Yang et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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