Arbitrarily rotating polarization direction and manipulating phases in linear and nonlinear ways using programmable metasurface

Liu, Wei; Wang, Si Ran; Dai, Jun Yan; Zhang, Lei; Chen, Qiao; Cheng, Qiang; Cui, Tie Jun

doi:10.1038/s41377-024-01513-2

Cited by 3 publications

References 53 publications

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A Reconfigurable Metasurface Antenna for Arbitrarily Polarized Beam Generation and Advanced Polarization Detection

Chen,

Wang,

Wang

et al. 2024

Laser & Photonics Reviews

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A reconfigurable metasurface antenna (RMA) is proposed that can be used as a full polarized beam launcher to generate arbitrarily polarized radiation waves, or as a polarization detector to identify the polarization state of unknown incoming wave. It is shown that integrating Positive‐Intrinsic‐Negative (PIN) diodes and varactor diodes on the metasurface can independently control the amplitudes and phases of two orthogonal polarization components of the radiated electromagnetic (EM) waves through bias voltage, further achieving real‐time control of the polarization states. The proposed RMA can also be used as a polarization detector to identify all possible polarization states of unknown incoming waves quickly. A prototype of RMA is designed and fabricated, and the functions of arbitrary polarization generation and detection are validated by experiments, which are consistent very well with the theoretical predictions. Due to the advantages of low profile, versatility, and fast response, the proposed RMA is promising to be used in the future wireless communication, radar, and so on.

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A Reconfigurable Metasurface Antenna for Arbitrarily Polarized Beam Generation and Advanced Polarization Detection

Chen,

Wang,

Wang

et al. 2024

Laser & Photonics Reviews

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Simplified radar architecture based on information metasurface

Cui,

Wang,

Chen

et al. 2024

Preprint

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Modern radar typically employs a chain architecture that consists of radio-frequency (RF) and intermediate frequency (IF) units, baseband digital signal processor, and information display. However, this architecture often results in high costs, significant hardware demands, and integration challenges. Here we propose a simplified radar architecture based on space-time-coding (STC) information metasurfaces. With their powerful capabilities to generate multiple harmonic frequencies and customize their phases, the STC metasurfaces play a key role in chirp signal generation, transmission, and echo reception. Remarkably, the receiving STC metasurface can implement dechirp processing directly on the RF level and realize the digital information outputs, which are beneficial to lower the hardware requirement at the receiving end while potentially shortening the time needed for conventional digital processing. As a proof of concept, the proposed metasurface radar is tested in a series of experiments for target detection and range/speed measurement, yielding results comparable to those obtained by conventional methods. This study provides valuable inspiration for a new radar system paradigm to combine the RF front ends and signal processors on the information metasurface platform that offers essential functionalities while significantly reducing the system complexity and cost.

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Explainable Encoder–Prediction–Reconstruction Framework for the Prediction of Metasurface Absorption Spectra

Ouyang,

Zeng,

Liu

2024

Nanomaterials

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The correlation between metasurface structures and their corresponding absorption spectra is inherently complex due to intricate physical interactions. Additionally, the reliance on Maxwell’s equations for simulating these relationships leads to extensive computational demands, significantly hindering rapid development in this area. Numerous researchers have employed artificial intelligence (AI) models to predict absorption spectra. However, these models often act as black boxes. Despite training high-performance models, it remains challenging to verify if they are fitting to rational patterns or merely guessing outcomes. To address these challenges, we introduce the Explainable Encoder–Prediction–Reconstruction (EEPR) framework, which separates the prediction process into feature extraction and spectra generation, facilitating a deeper understanding of the physical relationships between metasurface structures and spectra and unveiling the model’s operations at the feature level. Our model achieves a 66.23% reduction in average Mean Square Error (MSE), with an MSE of 2.843 × 10−4 compared to the average MSE of 8.421×10−4 for mainstream networks. Additionally, our model operates approximately 500,000 times faster than traditional simulations based on Maxwell’s equations, with a time of 3×10−3 seconds per sample, and demonstrates excellent generalization capabilities. By utilizing the EEPR framework, we achieve feature-level explainability and offer insights into the physical properties and their impact on metasurface structures, going beyond the pixel-level explanations provided by existing research. Additionally, we demonstrate the capability to adjust absorption by changing the metasurface at the feature level. These insights potentially empower designers to refine structures and enhance their trust in AI applications.

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Arbitrarily rotating polarization direction and manipulating phases in linear and nonlinear ways using programmable metasurface

Cited by 3 publications

References 53 publications

A Reconfigurable Metasurface Antenna for Arbitrarily Polarized Beam Generation and Advanced Polarization Detection

A Reconfigurable Metasurface Antenna for Arbitrarily Polarized Beam Generation and Advanced Polarization Detection

Simplified radar architecture based on information metasurface

Explainable Encoder–Prediction–Reconstruction Framework for the Prediction of Metasurface Absorption Spectra

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