Vision-driven metasurfaces for perception enhancement.

Metasurfaces have exhibited unprecedented degree of freedom in manipulating electromagnetic (EM) waves and thus provide fantastic front-end interfaces for smart systems. Here we show a framework for perception enhancement based on vision-driven metasurface. Human's eye movements are matched with microwave radiations to extend the humans' perception spectrum. By this means, our eyes can "sense" visual information and invisible microwave information. Several experimental demonstrations are given for specific implementations, including a physiological-signal-monitoring system, an "X-ray-glasses" system, a "glimpse-and-forget" tracking system and a speech reception system for deaf people. Both the simulation and experiment results verify evident advantages in perception enhancement effects and improving information acquisition efficiency. This framework can be readily integrated into healthcare systems to monitor physiological signals and to offer assistance for people with disabilities. This work provides an alternative framework for perception enhancement and may find wide applications in healthcare, wearable devices, search-and-rescue and others.

Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision.

The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize the smart beam tracking and wireless communications. Three groups of experiments are conducted for demonstrating the intelligent system: detection and identification of moving targets, detection of radio-frequency signals, and real-time wireless communications. The proposed method sets the stage for an integrated implementation of target identification, radio environment tracking, and wireless communications. This strategy opens up an avenue for intelligent wireless networks and self-adaptive systems.

Deep-Learning-Empowered Holographic Metasurface with Simultaneously Customized Phase and Amplitude.

Metasurfaces with simultaneously and independently controllable amplitude and phase have provided a higher degree of freedom in manipulating electromagnetic (EM) waves. Compared with phase- or amplitude-only modulation, the capability of simultaneously controlling the phase and amplitude of EM waves can enable holography with a higher resolution. However, this drastically increases the design complexity of holographic metasurfaces, and the design process is usually quite time-consuming. In this paper, we propose an inverse design of meta-atoms that can simultaneously and independently tailor the phase and amplitude of transmitted waves using customized deep ResNet while eliminating the coupling of parameters. To demonstrate the design method, two holographic metasurfaces were designed using the trained network without the need for parameter sweeping, which will significantly enhance design efficiency. Prototypes were fabricated and measured. Both the simulated and measured results show that high-resolution holography is obtained, which sufficiently verifies the reliability of the design method. Our work paves the way for the intelligent design of metasurfaces and can also be applied to the design of other artificial materials or surfaces.

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning.

Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 28×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.

Overcome chromatism of metasurface via Greedy Algorithm empowered by self-organizing map neural network.

Chromatism generally exists in most metasurfaces. Because of this, the deflected angle of metasurface reflectors usually varies with frequency. This inevitably hinders wide applications of metasurfaces to broadband signal scenarios. Therefore, it is of great significance to overcome chromatism of metasurfaces. With this aim, we firstly analyze necessary conditions for achromatic metasurface deflectors (AMD) and deduce the ideal dispersions of meta-atoms. Then, we establish a Self-Organizing Map (SOM) Neural Network as a prepositive model to obtain a diversified searching map, which is then applied to Greedy Algorithm to search meta-atoms with the required dispersions. Using these meta-atoms, an AMD was designed and simulated, with a thickness about 1/15 the central wavelength. A prototype was fabricated and measured. Both the simulation and measurement show that the proposed AMD can achieve an almost constant deflected angle of 22° under normal incidence within 9.5-10.5GHz. This method may find wide applications in designing functional metasurfaces for satellite communications, mobile wireless communications and others.

Deep Learning: A Rapid and Efficient Route to Automatic Metasurface Design.

Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time-consuming and computational resource-consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time-consuming, and less computational resource-consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple-band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

Achieving circular-to-linear polarization conversion and beam deflection simultaneously using anisotropic coding metasurfaces.

An anisotropic coding metasurface (CM) is proposed for achieving circular-to-linear polarization conversion and beam deflection. Different phase coding consequences were independently achieved for two orthogonal linear polarized (LP) waves. Thus by elaborately designing coding sequences of the metasurfaces, different functions can be achieved, respectively for waves polarized along two orthogonal directions. More importantly, for circularly polarized (CP) wave, anisotropic CM can achieve circular-to-linear polarization conversion and beam deflection simultaneously. As a proof, a 1-bit anisotropic CM with 0101…/0101… and 0000…/1111… coding sequences respectively for two orthogonal LP waves was designed. The simulation results indicated that the incident CP wave is deflected into two x-polarized waves in x-o-z plane and two y-polarized waves in y-o-z plane. Both the simulation and experimental results verify the circular-to-linear polarization conversion performance of the anisotropic coding metasurfaces. The proposed anisotropic coding metasurfaces have the potential for the applications of multifunctional devices.