Transmission-Reflection-Integrated Bifunctional Metasurface by Hybridizing Geometric Phase and Propagation Phase
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| Publicat a: | Electronics vol. 14, no. 21 (2025), p. 4250-4262 |
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| Autor principal: | |
| Altres autors: | , , , , , , |
| Publicat: |
MDPI AG
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resum: | Multifunctional metasurfaces, capable of flexible electromagnetic wave manipulation, have become a focus of research for their high integration and utility. In particular, those operating simultaneously in transmission and reflection modes have attracted growing interest, as they integrate multiple functions within a single aperture, save physical space, and further expand wave control capabilities across full space. In this work, an inspiring strategy of transmission-reflection-integrated bifunctional metasurface by hybridizing geometric phase and propagation phase is proposed. The transmission and reflection modes can be independently and flexibly controlled in full space: the co-polarized reflection under left-handed circular polarization (LCP) incidence is governed by rotation-induced geometric phase modulation, while the co-polarized transmission under right-handed circular polarization (RCP) incidence is modulated through scaling-induced propagation phase modulation. Moreover, arbitrary amplitude modulation of the co-polarized transmission under RCP incidence can be realized by incorporating lumped resistors. As a proof of concept, a bifunctional meta-device is constructed, which can generate vortex beam carrying arbitrary topological charge for LCP reflected wave and achieve high-quality holographic imaging for RCP transmitted wave. Both the simulated and experimental results validate the feasibility of the proposed strategy, which significantly enhances the integration density of multifunctional metasurfaces while reducing inter-functional crosstalk, expanding its potential applications in electronic engineering. Moreover, it can also serve as a fundamental machine learning platform, facilitating multimodal fusion and cross-modal learning in radar signals and visual imaging. |
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| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics14214250 |
| Font: | Advanced Technologies & Aerospace Database |