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We propose, design, and experimentally demonstrate a nonlocal metasurface with frequency-selective, wavefront shaping capabilities and at the same time polarization-selective chiral response. This operation requires the implementation of bilayer metasurfaces with engineered nonlocal response, wherein each layer controls locally a specific linear polarization, while the coupled system supports arbitrary polarization states. We demonstrate that this platform enables unprecedented control over wavefront manipulation, including frequency-selective, spin-selective reflection with arbitrary geometric phase. We observe a highly chiral response with record-high reflectance efficiency over a narrow frequency window, both for a uniform metasurface and for one with tailored phase gradient for anomalous reflection. Both devices provide an efficiency well above the theoretical limit of 25% for conventional single-layer devices. Our work opens exciting opportunities for augmented reality and enhanced secure wireless communications.
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Real-data-driven real-time reconfigurable microwave reflective surface
Abstract Manipulating the electromagnetic (EM) scattering behavior from an arbitrary surface dynamically on arbitrary design goals is an ultimate ambition for many EM stealth and communication problems, yet it is nearly impossible to accomplish with conventional analysis and optimization techniques. Here we present a reconfigurable conformal metasurface prototype as well as a workflow that enables it to respond to multiple design targets on the reflection pattern with extremely low on-site computing power and time. The metasurface is driven by a sequential tandem neural network which is pre-trained using actual experimental data, avoiding any possible errors that may arise from calculation, simulation, or manufacturing tolerances. This platform empowers the surface to operate accurately in a complex environment including varying incident angle and operating frequency, or even with other scatterers present close to the surface. The proposed data-driven approach requires minimum amount of prior knowledge and human effort yet provides maximized versatility on the reflection control, stepping towards the end form of intelligent tunable EM surfaces.
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- Award ID(s):
- 2148318
- PAR ID:
- 10492167
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41467-023-43473-y
