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.

The power of controlling objects with mind has captivated a popular fascination to human beings. One possible path is to employ brain signal collecting technologies together with emerging programmable metasurfaces (PM), whose functions or operating modes can be switched or customized via on-site programming or pre-defined software. Nevertheless, most of existing PMs are wire-connected to users, manually-controlled and not real-time. Here, we propose the concept of remotely mind-controlled metasurface (RMCM) via brainwaves. Rather than DC voltage from power supply or AC voltages from signal generators, the metasurface is controlled by brainwaves collected in real time and transmitted wirelessly from the user. As an example, we demonstrated a RMCM whose scattering pattern can be altered dynamically according to the user’s brain waves via Bluetooth. The attention intensity information is extracted as the control signal and a mapping between attention intensity and scattering pattern of the metasurface is established. With such a framework, we experimentally demonstrated and verified a prototype of such metasurface system which can be remotely controlled by the user to modify its scattering pattern. This work paves a new way to intelligent metasurfaces and may find applications in health monitoring, 5G/6G communications, smart sensors, etc.

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In this paper, we propose a method of designing ultra-wideband single-layer metasurfaces for cross-polarization conversion, via the introduction of Fano resonances. By adding sub-branches onto the unit cell structure, the induced surface currents are disturbed, leading to coexistence of both bright and dark modes at higher frequencies. Due to the strong interaction between the two modes, Fano resonance can be produced. In this way, five resonances in all are produced by the single-layer metasurface. The first four are conventional and are generated by electric and magnetic resonances, whereas the fifth one is caused by Fano resonance, which further extends the bandwidth. A prototype was designed, fabricated and measured to verify this method. Both the simulated and measured results show that a 1:4.4 bandwidth can be achieved for both x- and y-polarized waves, with almost all polarization conversion ratio (PCR) above 90%. This method provides an effective alternative to metasurface bandwidth extension and can also be extended to higher bands such as THz and infrared frequencies.

Multifunctional metasurfaces have exhibited considerable abilities of manipulating electromagnetic (EM) waves, especially in full-space manipulation. However, most works are implemented with functions controlled by polarization or frequency and seldom involve the incidence angle. Herein, we propose a multifunctional full-space metasurface controlled by frequency, polarization and incidence angle. A meta-atom is firstly designed. When EM waves illumine normally in the C-band, it possesses the characteristic of asymmetric transmission with high-efficient polarization conversion. In the Ku-band, both x- and y-polarized EM waves along both sides will be reflected and achieve broadband and high-efficient cross-polarization conversion. Also, when illumined obliquely, both sides can achieve efficient retroreflection at a certain frequency. As a proof of concept, a metasurface consisting of the above meta-atoms is configured as a dual orbital angular momentum (OAM) vortex beam generator and different beam deflector when illumined normally. Meanwhile, it acts as a multi-channel retroreflector when illumined obliquely. Both the simulated and measured results show excellent performances. Our findings provide a new degree of freedom to design multifunctional metasurfaces that can further promote applications.

For camouflage applications, the performance requirements for metamaterials in different electromagnetic spectra are usually contradictory, which makes it difficult to develop satisfactory design schemes with multispectral compatibility. Fortunately, empowered by machine learning, metamaterial design is no longer limited to directly solving Maxwell’s equations. The design schemes and experiences of metamaterials can be analyzed, summarized, and learned by computers, which will significantly improve the design efficiency for the sake of practical engineering applications. Here, we resort to the machine learning to solve the multispectral compatibility problem of metamaterials and demonstrate the design of a new metafilm with multiple mechanisms that can realize small microwave scattering, low infrared emissivity, and visible transparency simultaneously using a multilayer backpropagation neural network. The rapid evolution of structural design is realized by establishing a mapping between spectral curves and structural parameters. By training the network with different materials, the designed network is more adaptable. Through simulations and experimental verifications, the designed architecture has good accuracy and robustness. This paper provides a facile method for fast designs of multispectral metafilms that can find wide applications in satellite solar panels, aircraft windows, and others.

The modern design of advanced functional materials or surfaces has increasingly undergone miniaturization and integration, so as to implement multiple functions using the same aperture. Metasurfaces, as an emerging kind of artificial functional surface, have provided unprecedented freedom in manipulating electomagnetic (EM) waves upon two-dimensional surfaces. It is desirable that the aperture of metasurfaces can be multiplexed with a number of functions for EM waves. Based on previous research, an artificial neural network-forward design can achieve an inverse design. In this paper, we propose an inverse design method of multiplexing metasurface apertures. A deep learning network (DLN) is trained to serve as a forward model for a genetic algorithm (GA), that is, a deep-leaning-forward genetic algorithm (DLF-GA). With the DLF-GA model, the phase of meta-atoms can be predicted for orthogonally linearly polarized waves simultaneously. The DLN was trained by a data set consisting of 70 000 samples with an accuracy of 95% on the test data. To demonstrate the competency of this method, we demonstrate the design of a multiplexed metasurface that can achieve focusing and diffuse scattering for polarization. The metasurface, which consists of 24 × 24 meta-atoms, can be generated monolithically with the input phase profile. A prototype was fabricated and measured. The predicted, simulated, and measured results are well consistent and underscore the validity of this inverse design method. This method provides an efficient and accurate method for the fast design of multiplexed metasurfaces and will find applications in microwave engineering such as in satellite communications.

Amplitude–phase control for circular polarized (CP) waves is experiencing a research upsurge in electromagnetics owing to the kaleidoscopic electromagnetic responses and promising application prospects of circular polarizations, and chiral metasurfaces are more facile to achieve a series of intriguing chiral phenomena than natural materials. However, it is difficult for most existing chiral metasurfaces to independently tailor the amplitude and phase of left-handed circular polarized and right-handed circular polarized waves at the same frequency as they suffer the drawbacks of large thickness, multiple layers, and complex structure. Herein, an innovative strategy of single-layer achiral metasurfaces of thickness 0.13λ0 is proposed to independently and simultaneously manipulate the amplitude and phase of orthogonal CP waves. As a proof of concept, an amplitude and phase controlled dual-channel meta-hologram is designed to reconstruct diverse images with high fidelity under orthogonal CP illumination, and the simulated and experimental results collectively validate the availability of our methodology. Significantly, the meta-hologram is also applicable to full polarization states according to the decomposition of electromagnetic waves. The inspiring design of single-layer achiral metasurfaces provides a simple and effective approach to explore chiral effects, and they possess enormous application potential in multitudinous microwave devices.