Complete-basis-reprogrammable coding metasurface for generating dynamicallycontrolled holograms under arbitrary polarization states

Reprogrammable metasurfaces,which establish a fascinating bridge between physical and information domains,can dynamically control electromagnetic(EM)waves in real time and thus have attracted great attentions from researchers around the world.To control EM waves with an arbitrary polarization state,it is desirable that a complete set of basis states be controlled independently since incident EM waves with an arbitrary polarization state can be decomposed as a linear sum of these basis states.In this work,we present the concept of complete-basis-reprogrammable coding metasurface(CBR-CM)in reflective manners,which can achieve independently dynamic controls over the reflection phases while maintaining the same amplitude for left-handed circularly polarized(LCP)waves and right-handed circularly polarized(RCP)waves.Since LCP and RCP waves together constitute a complete basis set of planar EM waves,dynamicallycontrolled holograms can be generated under arbitrarily polarized wave incidence.The dynamically reconfigurable metaparticle is implemented to demonstrate the CBR-CM’s robust capability of controlling the longitudinal and transverse positions of holograms under LCP and RCP waves independently.It’s expected that the proposed CBR-CM opens up ways of realizing more sophisticated and advanced devices with multiple independent information channels,which may provide technical assistance for digital EM environment reproduction.

Direct field-to-pattern monolithic design of holographic metasurface via residual encoderdecoder convolutional neural network

Complex-amplitude holographic metasurfaces(CAHMs)with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level,leading to higher image-reconstruction quality compared with their natural counterparts.However,prevailing design methods of CAHMs are based on Huygens-Fresnel theory,meta-atom optimization,numerical simulation and experimental verification,which results in a consumption of computing resources.Here,we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design.A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory,which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images.The training results show that the normalized mean pixel error is about 3%on dataset.As verification,the metasurface prototypes are fabricated,simulated and measured.The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field,which demonstrates the effectiveness of our design.Encouragingly,this work provides a monolithic field-to-pattern design method for CAHMs,which paves a new route for the direct reconstruction of metasurfaces.

Six-channel programmable coding metasurface simultaneously for orthogonal circular and linear polarizations

Metasurfaces have intrigued long-standing research interests and developed multitudinous compelling applications owing to their unprecedented capability for manipulating electromagnetic waves,and the emerging programmable coding metasurfaces(PCMs)provide a real-time reconfigurable platform to dynamically implement customized functions.Nevertheless,most existing PCMs can only act on the single polarization state or perform in the limited polarization channel,which immensely restricts their practical application in multitask intelligent metadevices.Herein,an appealing strategy of the PCM is proposed to realize tunable functions in co-polarized reflection channels of orthogonal circularly polarized waves and in co-polarized and cross-polarized reflection channels of orthogonal linearly polarized waves from 9.0 to 10.5 GHz.In the above six channels,the spindecoupled programmable meta-atom can achieve high-efficiency reflection and 1-bit digital phase modulation by selecting the specific ON/OFF states of two diodes,and the phase coding sequence of the PCM is dynamically regulated by the field-programmable gate array to generate the desired function.A proof-of-concept prototype is constructed to verify the feasibility of our methodology,and numerous simulation and experimental results are in excellent agreement with the theoretical predictions.This inspiring design opens a new avenue for constructing intelligent metasurfaces with higher serviceability and flexibility,and has tremendous application potential in communication,sensing,and other multifunctional smart metadevices.

Leaky cavity modes in metasurfaces:a route to low-loss wideband anomalous dispersion

Metasurfaces have provided unprecedented degrees of freedom in manipulating electromagnetic waves upon interfaces.In this work,we first explore the condition of wide operating bandwidth in the view of reflective scheme,which indicates the necessity of anomalous dispersion.To this end,the leaky cavity modes(LCMs)in the metaatom are analyzed and can make effective permittivity inversely proportional to frequency.Here we employ the longitudinal Fabry-Perot(F-P)resonances and transverse plasmonic resonances to improve the LCMs efficiency.It is shown that the order of F-P resonance can be customized by the plasmonic modes,that is,the F-P cavity propagation phase should match the phase delay of surface currents excited on the meta-atom.The nth order F-P resonance will multiply the permittivity by a factor of n,allowing larger phase accumulation with increasing frequencies and forming nonlinear phase distribution which can be applied in weak chromatic-aberration focusing design.As a proof-of-concept,we demonstrate a planar weak chromatic-aberration focusing reflector with a thickness ofλ_(0)∕9 at 16.0-21.0 GHz.This work paves a robust way to advanced functional materials with anomalous dispersion and can be extended to higher frequencies such as terahertz,infrared,and optical frequencies.

Virtual metasurfaces:reshaping electromagnetic waves in distance

Metasurface has provided unprecedented freedoms in manipulating electromagnetic(EM) waves, exhibiting fascinating functions. Conventionally, these functions are implemented right on metasurfaces, where spatial modulations on EM wave amplitudes or phases are achieved by meta-atoms. This study proposes the concept of virtual metasurface(VM), which is formed by arrays of foci away from the entity metasurface. Unlike conventional metasurfaces, spatial modulations on the amplitudes or phases of EM waves occur in the air, with a focal length distance from the entity metasurface. As a proof of concept, we demonstrated a transmissive VM. The entity metasurface consists of transmissive focusing metasurface tiles(TFMTs) with the same focal length. Two TFMTs were designed with phase difference π to enable the most typical checkerboard configuration. The TFMTs were assembled to form the entity metasurface, whereas their foci formed the VM. Due to the π phase difference among adjacent foci, EM propagation along the normal direction was cancelled, leading to four tilted far-field beams. The concept of VM can be readily extended to higher frequencies from terahertz to optical regimes and may find wide applications in communication, camouflage, and other fields.

Machine-learning-empowered multispectral metafilm with reduced radar cross section,low infrared emissivity,and visible transparency

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 engineer-ing 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 back-propagation 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.

Remotely mind-controlled metasurface via brainwaves

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.

Improved atmospheric mercury simulation using updated gas-particle partition and organic aerosol concentrations

The gaseous or particulate forms of divalent mercury(HgⅡ) significantly impact the spatial distribution of atmospheric mercury concentration and deposition flux(FLX). In the new nested-grid GEOS-Chem model, we try to modify the HgⅡ gas-particle partitioning relationship with synchronous and hourly observations at four sites in China. Observations of gaseous oxidized Hg(GOM), particulate-bound Hg(PBM), and PM 2.5 were used to derive an empirical gas-particle partitioning coefficient as a function of temperature( T) and organic aerosol(OA) concentrations under different relative humidity(RH). Results showed that with increasing RH, the dominant process of HgⅡ gas-particle partitioning changed from physical adsorption to chemical desorption. And the dominant factor of HgⅡ gas-particle partitioning changed from T to OA concentrations. We thus improved the simulated OA concentration field by introducing intermediate-volatility and semi-volatile organic compounds(I/SVOCs) emission inventory into the model framework and refining the volatile distributions of I/SVOCs according to new filed tests in the recent literatures. Finally, normalized mean biases(NMBs) of monthly gaseous element mercury(GEM), GOM, PBM, WFLX were reduced from-33%–29%, 95%–300%, 64%–261%, 117%–122% to-13%–0%,-20%–80%,-31%–50%,-17%–23%. The improved model explains 69%–98% of the observed atmospheric Hg decrease during 2013–2020 and can serve as a useful tool to evaluate the effectiveness of the Minamata Convention on Mercury.