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Recently, reconfigurable intelligent surfaces (RISs) have attracted extensive attentions from the communication community due to their powerful wavefront manipulation and signal modulation abilities. Optically transparent RISs are needed in some scenarios requiring both optical transparency and high‐quality communication services. In RIS‐based wireless communications, the channel reciprocity is an important factor to be considered since the electromagnetic responses of RISs usually depend on the incident and receiving angles. To address this problem, an optically transparent 2‐bit RIS with low angular sensitivity is proposed. By applying a transparent dielectric substrate and metal‐mesh‐based patterns, the designed RIS achieves a light transmittance of 49.3%. Both the simulation and measurement results demonstrate the low angular sensitivity at transverse magnetic incidence from 0° to 60°. Moreover, beam steering experiments under various coding sequences and various incident angles are simulated and measured, and the results are consistent with the theoretically predicted results. A further study on the broadcast mode of the RIS shows that its phase response is weakly dependent to the incident and receiving directions. [ABSTRACT FROM AUTHOR]

Due to its ability to adapt to a variety of electromagnetic (EM) environments, the sensing‐enabled metasurface has garnered significant attention. However, large‐scale EM‐field sensing to obtain more information is still very challenging. Here, an adaptive information metasurface is proposed to enable intelligent sensing and wave manipulating simultaneously or more specifically, to realize intelligent target localization and beam tracking adaptively. The metasurface is composed of an array of meta‐atoms, and each is loaded with two PIN diodes and a sensing‐channel structure, for polarization‐insensitive and programmable beamforming and sensing. By controlling the state of the PIN diode, the proposed meta‐atom has 1‐bit phase response in the designed frequency band, while the sensing loss keeps higher than ‐10 dB for both "ON" and "OFF" states. Hence there is nearly no interaction between the beamforming and sensing modes. Experiments are conducted to show multiple functions of the metasurface, including intelligent target sensing and self‐adaptive beamforming, and the measured results are in good agreement with the numerical simulations and theoretical calculations. [ABSTRACT FROM AUTHOR]

Multi‐functional metasurfaces have shown great promise in tackling complex electromagnetic issues. Reconfigurable frequency‐selective Rasorbers (FSRs) have recently received growing attraction, but existing designs are constrained in their switchable modes, typically limited to FSR/Absorber or FSR/frequency‐selective surface (FSS). To address this limitation, a new metasurface that integrates four different functioning modes, including FSR, FSS, Absorber, and Reflector is proposed, which can be dynamically switched as demanded. The design is based on the theoretical network model analysis, and its intriguing performances are verified through simulations and experiments both in frequency and time domains. The results show that the FSR and Absorber modes exhibit wide low‐scattering bandwidths with narrow transmission windows that can be turned on or off. These two modes also eliminate the sputtering effect, making it suitable for stealth applications. In contrast, the Reflector mode is efficient in blocking microwaves across a broad spectrum, and a transmission window can be opened in the FSS mode. It is believed this multi‐functional metasurface can serve as a radome to protect against various challenges such as detection, interference, and high‐power invasion. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) and deep learning (DL) have been widely used to break the metasurface’s performance ceiling. However, the existing data‐driven ML and DL methods usually require the availability of vast amounts of training data to ensure their stable and accurate performance. The process of acquiring these data is high‐cost due to the need for numerous full‐wave electromagnetic (EM) simulations. Here, we  propose a low‐cost surrogate model to generate these data efficiently. The proposed model employs microwave network theory to separate meta‐elements into four independent components. Through integration with transmission line theory, we  derive the EM responses of meta‐elements using analytical representation with the active device equivalent impedance and dielectric as design variables. Two typical phase‐modulation active meta‐elements are employed to verify the accuracy of our  macromodel in comparison with full‐wave EM simulations. Based on the developed macromodel, the superior prediction ability is further presented to illustrate the performance of meta‐elements with various active devices and dielectric substrates. The proposed macromodel is a feasible and general method to rapidly obtain the necessary training data of active meta‐elements, which holds a great potential to significantly reduce the designing time of ML and DL models for the active metasurfaces. [ABSTRACT FROM AUTHOR]