Modern-day data science, together with physical science, is reshaping the landscape of artificial electromagnetic media-metasurfaces-on a scale not seen before. Such interaction excels in computationally intensive tasks and real-world applications, such as inverse design, spectral analysis, autonomous devices, and neuromorphic computing. Here, we foreground the rise of intelligent adaptive metasurfaces that are renovating our understanding and utilization of metasurfaces, moving away from human-based control toward automatic control for real-time updates of application requirements. To make the most of these emerging opportunities, we also comment on the perspectives of intelligent adaptive metasurfaces.

Direction of arrival (DoA) estimation is a key research focus in array signal processing, and numerous progressive direction-finding algorithms have already been developed. In terms of the development of algorithms, metasurfaces can help innovate traditional estimation algorithms as an excellent alternative to phased arrays. New types of artificial intelligence continue to impact traditional algorithms as well as the detection of the incoming wave direction. Miniaturized and integrated incoming wave estimation devices suitable for various systems have become a significant trend in hardware implementation. In this study, the latest progress and trends in this emerging field are reviewed, and their potential value is assessed. First, a brief overview of a combination of classical DoA algorithms and metasurface is presented. Based on this, the applications of common subspace and sparse representation methods were surveyed, followed by a discussion of their potential prospects. The use of artificial intelligence combined with metasurfaces to innovate DoA detection is discussed. Finally, challenges and opportunities for advancing metasurfaces and artificial intelligence in this frontier field are discussed.

Convective thermal metamaterials are artificial structures where convection dominates in the thermal process. Due to the field coupling between velocity and temperature, convection provides a new knob for controlling heat transfer beyond pure conduction, thus allowing active and robust thermal modulations. With the introduced convective effects, the original parabolic Fourier heat equation for pure conduction can be transformed to hyperbolic. Therefore, the hybrid diffusive system can be interpreted in a wave-like fashion, reviving many wave phenomena in dissipative diffusion. Here, recent advancements in convective thermal metamaterials are reviewed and the state-of-the-art discoveries are classified into the following four aspects, enhancing heat transfer, porous-media-based thermal effects, nonreciprocal heat transfer, and non-Hermitian phenomena. Finally, a prospect is cast on convective thermal metamaterials from two aspects. One is to utilize the convective parameter space to explore topological thermal effects. The other is to further broaden the convective parameter space with spatiotemporal modulation and multi-physical effects.

Polarization, as a vector nature of the electromagnetic wave, plays a fundamental role in optics. Determining the polarization state of light is required by many applications, spanning from remote sensing and material analysis to biology and microscopy. To achieve this goal, conventional methods necessitate cascading of multiple optical components and consequential measurements to estimate the Stokes parameters, rendering the entire optical system bulky, complex, and sensitive. Here a brand-new strategy is introduced for direct polarization readout based on dual-channel neuro-metasurfaces. Neuro-metasurfaces can independently manipulate two orthogonal linearly-polarized waves that can synthesize arbitrary polarization waves with a linear combination. By judiciously designing the output focus points, a unique polarization atlas is created that allows one-to-one correspondence from intensity ratio to polarization state. To implement this, polarization-sensitive metasurfaces are designed and the spatial layout is optimized using a diffractive neural network. The feasibility of this strategy is validated by numerical simulation and microwave experiments. These results pave a new avenue in realizing integrated and multifunctional detectors and demonstrate the potential of neuro-metasurfaces as an add-on for discomposing and composing spatial basis. © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.

Optical logic operations lie at the heart of optical computing, and they enable many applications such as ultrahigh-speed information processing. However, the reported optical logic gates rely heavily on the precise control of input light signals, including their phase difference, polarization, and intensity and the size of the incident beams. Due to the complexity and difficulty in these precise controls, the two output optical logic states may suffer from an inherent instability and a low contrast ratio of intensity. Moreover, the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is considered. As such, it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic system. Such a goal remains challenging. Here, we introduce a simple yet universal design strategy, capable of using plane waves as the incident signal, to perform optical logic operations via a diffractive neural network. Physically, the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers, namely, a compound Huygens' metasurface. That is, the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer, which provides the information of output logic states. Importantly, after training of the diffractive neural network, all seven basic types of optical logic operations can be realized by the same metasurface. As a conceptual illustration, three logic operations (NOT, OR, and AND) are experimentally demonstrated at microwave frequencies.