Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing. From passive to active, metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic (EM) wavefields. However, the programmable metasurfaces require manual control to switch among different functionalities. Here, we put forth a smart metasurface that has self-adaptively reprogrammable functionalities without human participation. The smart metasurface is capable of sensing ambient environments by integrating an additional sensor(s) and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system. As an illustrative example, we experimentally develop a motion-sensitive smart metasurface integrated with a three-axis gyroscope, which can adjust self-adaptively the EM radiation beams via different rotations of the metasurface. We develop an online feedback algorithm as the control software to make the smart metasurface achieve single-beam and multibeam steering and other dynamic reactions adaptively. The proposed metasurface is extendable to other physical sensors to detect the humidity, temperature, illuminating light, and so on. Our strategy will open up a new avenue for future unmanned devices that are consistent with the ambient environment.

There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time, we propose the simultaneous use of a smart metasurface imager and recognizer, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy, transforming measured microwave data into images of the whole human body, classifying specifically designated spots (hand and chest) within the whole image, and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities, smart homes, human-device interaction interfaces, health monitoring, and safety screening free of visual privacy issues.

Digital coding metasurfaces are aimed at simplifying the design and optimization procedures, and manipulating electromagnetic waves in digital manner. In this paper, a multilayered anisotropic coding metasurface is designed to realize multiple independent functionalities by changing the polarization and direction of incident waves. As a proof of concept, the beam deflection, diffuse scattering, and vortex beam generation are realized by using only a single transmission‐reflection‐integrated (TRI) coding metasurface. This design can achieve three different functionalities and simultaneous controls of transmitted and reflected wavefronts on a shared aperture with the TRI coding scheme. Both numerical and measured results verify the excellent performance of the multifunctional digital coding metasurface, which provides a simple way to extend the functionality of high‐efficiency metadevices.

Artificial intelligence is facilitating human life in many aspects. Previous artificial intelligence has been mainly focused on computer algorithms (e.g. deep-learning and extremelearning) and integrated circuits. Recently, all-optical diffractive deep neural networks (D 2 NN) were realized by using passive structures, which can perform complicated functions designed by computer-based neural networks at the light speed. However, once a passive D 2 NN architecture is fabricated, its function will be fixed. Here, we propose a programmable artificial intelligence machine (PAIM) that can execute various intellectual tasks by realizing hierarchical connections of brain neurons via a multi-layer digital-coding metasurface array. Integrated with two amplifier chips in each meta-atom, its transmission coefficient covers a dynamic range of 35 dB (from -40 dB to -5 dB), which is the basis to construct the reprogrammable physical layers of D 2 NN, in which the digital meta-atoms make the artificial neurons alive. We experimentally show that PAIM can handle various deep-learning tasks for wave sensing, including image classifications, mobile communication coder-decoder, and real-time multi-beam focusing. In particular, we propose a reinforcement learning algorithm for on-site learning and discrete optimization algorithm for digital coding, making PAIM have autonomous intelligence ability and perform self-learning tasks without the support of extra computer.

In modern wireless communications, digital information is firstly converted to analog signal by a digital-analog convertor, which is then mixed to high-frequency microwave to be transmitted through a series of devices including modulator, mixer, amplifier, filter, and antenna and is finally received by terminals via a reversed process. Although the wireless communication systems have evolved significantly over the past thirty years, the basic architecture has not been challenged. Here, we propose a method to transmit digital information directly via programmable coding metasurface. Since the coding metasurface is composed of ‘0’ and ‘1’ digital units with opposite phase responses, the digital information can be directly modulated to the metasurface with certain coding sequences and sent to space under the illumination of feeding antenna. The information, being modulated in radiation patterns of the metasurface, can be correctly received by multiple receivers distributed in different locations. This method provides a completely new architecture for wireless communications without using complicated digital-analog convertor and a series of active/passive microwave devices. We build up a prototype to validate the new architecture experimentally, which may find promising applications where information security is highly demanded.