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Inverse design of dual-bandpass metasurface filters empowered by the multi-objective genetic algorithm.

Authors :
Yu, Ke
Ge, Jiahao
Li, Haonan
Zhang, Yaqiang
Dong, Hongxing
Zhang, Long
Source :
Optics Communications. Sep2024, Vol. 566, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Multi-band metasurface filters are becoming increasingly pivotal in high-capacity communication technologies. Traditional methods for designing metasurface structures, to date, have relied on empirical approaches to obtain target electromagnetic responses, thereby suffering from low efficiency. Here, we demonstrate an advanced approach for the inverse design of a multi-band metasurface filter, which consists of a multi-objective genetic algorithm (MOGA) in tandem with equivalent circuit model (ECM) analysis. This integration converts specific frequency response requirements into ECM parameter constraints, significantly streamlining the metasurface design and optimization process and offering superior solutions. Using this inverse design method, we theoretically propose a dual-bandpass metasurface filter which can exhibit transmission passbands in any two adjacent frequency ranges among the X-, Ku-, and K-bands. Further, numerical simulations validate the performances of the proposed device, which show great agreement with MOGA-based predictions. Our results pave the way to the effective inverse design of multi-passband metasurface filters which are useful in many applications, such as microwave filters, radar and satellite communication systems, and radio frequency identification devices. • A novel methodology combining MOGA and ECM is proposed for the inverse design of dual-bandpass metasurface filters. • The expected operating frequency of the proposed metasurface filter is switchable within the X-, Ku-, and K-bands. • The device is insensitive to the polarization and incident angle. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00304018
Volume :
566
Database :
Academic Search Index
Journal :
Optics Communications
Publication Type :
Academic Journal
Accession number :
177754642
Full Text :
https://doi.org/10.1016/j.optcom.2024.130695