Being invisible at will has been a long-standing dream for centuries, epitomized by numerous legends; humans have never stopped their exploration steps to realize this dream. Recent years have witnessed a breakthrough in this search due to the advent of transformation optics, metamaterials, and metasurfaces. However, the previous metasurface cloaks typically work in a reflection manner that relies on a high-reflection background, thus limiting the applications. Here, we propose an easy yet viable approach to realize the transmitted metasurface cloak, just composed of two planar metasurfaces to hide an object inside, such as a cat. To tackle the hard-to-converge issue caused by the nonuniqueness phenomenon, we deploy a tandem neural network (T-NN) to efficiently streamline the inverse design. Once pretrained, the T-NN can work for a customer-desired electromagnetic response in one single forward computation, saving a great amount of time. Our work opens a new avenue to realize a transparent invisibility cloak, and the tandem-NN can also inspire the inverse design of other metamaterials and photonics.

Optical illusion has always attracted extensive attention, as it provides a superior self‐protection ability for both natural animals and human beings. A decade ago, this motivated the study and application of transformation optics, which provides a universal tool to manipulate light for invisibility cloaking and optical illusion. However, mainstream transformation‐optics‐based optical illusions are inherently hindered by the extreme requirements of metamaterial compositions in practice and unfavorably limited by the very large computational cost caused by their bulky state. To overcome these grand challenges, a novel and intelligent optical illusion supported by form‐free metasurfaces via a deep learning architecture is reported, which can not only render a similar illusion effect but also greatly reduces the parameter space in physics. Illustrative examples of conformal metasurfaces are presented, with a high‐fidelity inverse design from either the near‐ or far‐field in the simulation and experiment. Furthermore, a full set of intelligent systems is developed to benchmark the real‐world optical illusion applicability. The work brings the available illusion strategies closer to a wide range of in situ practical‐oriented applications and lays a foundation for the next generation of intelligent metamaterials.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.

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Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the “parent” metasurface and then is freely assembled to construct the “offspring” metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.

formation optics render an object invisible by guiding the flow of light around the hidden object without disturbance to the internal region. [2][3][4][5][6] The underlying physics is attributed to the form invariance of Maxwell's equations: a coordinate transformation can squeeze normal free space from a volume into a shell, only with volumetric constitutive parameters and electromagnetic (EM) fields. Not only in electromagnetics, but transformation optics have also thus far been evolved into a fashionable tool in the realm of sound, heat flow, water waves, and so on. In theory, this method is perfect; however, in an experiment, it is hampered by the bulky material compositions with both anisotropy and inhomogeneity. Substantial efforts have been devoted to mitigating the requirements, such as bilinear transformation optics without singularities and quasi-conformal transformations for ground-plane cloak. [7][8][9][10][11][12][13] Yet, these tradeoffs also impair the cloaking performance to some extend and make the application scenario become more specific.Metasurface cloak is another kind of cloaking methodology that develops contemporaneously. [14][15][16] By adding a layer of deliberately-designed metasurfaces over a hidden region or object, the scattered field can be reconstructed to be similar to that of the pure background, as if there were no region or object. [17][18][19][20][21] Compared with a bulky metamaterial cloak, a metasurface cloak has the distinct advantages of negligible thickness, easy fabrication, and low loss, ushering it popularity in both academia and industry. [22][23][24][25][26] The last several years have seen enormous progress to demonstrate metasurface cloak across different spectra and generalize it from reflection to transmission geometries. [27] Moreover, by incorporating active components and intelligent algorithms, it is promising to transform conventional static cloaking modality to dynamic cloaking that can self-direct to ever-changing external stimuli and environment. [28,29] These advancements are highly demanded for a multitude of practical applications involving moving objects and dynamic environments.Although the current arsenal of design techniques provides enormous capability, metasurface cloak, as well as other cloaking strategies, suffers from some inherent limitations that need to be lifted or pushed, [15] as schematically illustrated in Figure 1a. First, we notice that almost all previous metasurface cloaks have been demonstrated in a convex shape. [19,22,30] The breakthroughs of transformation optics and metamaterials have kickstarted the study of modern invisibility cloak since the beginning of this century. Many cloaking methodologies have been progressively proposed for specific application scenarios, among which metasurface cloak is largely welcomed owing to its salient features of negligible thickness, easy fabrication, and low loss. Similar to other cloaking methodologies, however, metasurface cloak suffers from inherent limits that impair it to a convex shap...

Advanced wireless communication with high spectrum efficiency and energy efficiency has always fascinated humanity, especially with the explosive increase of global mobile data services. Index modulation (IM) has recently been found to be a promising technique due to the transmission of additional data bits via the indices of the available transmit entities. However, the practical implementation of IM remains a great challenge associated with complicated radio components. Herein, IM with intelligent spatiotemporal metasurfaces is experimentally demonstrated. The spatiotemporal metasurfaces provide a natural and versatile platform to achieve IM in a green and lightweight manner. The whole system is driven by a built‐in inverse‐design agent that automates spatiotemporal metasurfaces to cater to diverse application demands. In doing so, how to mitigate the inherent nonuniqueness issue and how to setup the input target from practical scenes are concretely discussed. In the microwave experiment, the spatiotemporal metasurfaces are fabricated and demonstrate the feasibility by harvesting two harmonic waves as communication channels. An intelligent electromagnetic platform that can manipulate electromagnetic waves in multidimensions is provided, meriting other numerous intelligent meta‐devices that avoid overburdening data analysis networks in smart cities of the future.