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Broadband Intelligent Programmable Metasurface with Polarization-Modulated Self-Adaptively Electromagnetic Functionality Switching

preprint
posted on 2024-02-02, 10:20 authored by Ximing Li, Rui Xu, GuoHong Du, XiaoFeng Sun, Yuan Zhao, Zhao Yang
Programmable metasurfaces have received a great deal of attention due to their ability to dynamically manipulate electromagnetic (EM) waves. Despite the rapid growth, most of the existing metasurfaces require manual control to switch among different functionalities, which poses severe limitations on practical applications. Here, we put forth an intelligent metasurface that has self-adaptively EM functionality switching in broadband without human participation. It is equipped with polarization discrimination antennas (PDAs) and feedback components to automatically adjust functionalities for the different incident polarization information. The PDAs module can first perceive the polarization of incident EM waves, e.g., linear or circular polarization, and then provide the feedback signal to the controlling platform for switching the EM functionality. As exemplary demonstrations, a series of functionalities in 9-22 GHz bands have been realized, including beam scanning for x-polarization, specular reflection for y-polarization, diffuse scattering for left-handed circular polarization (LCP), and vortex beam generation for right-handed circular polarization (RCP) wave. Experiments verify the good self-adaptive reaction capability of the intelligent metasurface and are in good agreement with the designs. Our strategy provides an avenue toward future unmanned devices that are consistent with the ambient environment.

History

Funder Name

CMA Key Laboratory of Atmospheric Sounding Program ( U2021M01); Program of Key Laboratory of Wireless Power Transfer, Ministry of Education (2022Z256); Program of Key Laboratory of Wireless Power Transfer, Ministry of Education (2023Z016); Foundation of Chengdu University of Information Technology under Grant (KYTZ202225); Foundation of Chengdu University of Information Technology under Grant (KYTZ202228)

Preprint ID

111632

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