Volume 12, Issue 20

Tunable nanophotonic metastructures offer new capabilities in computing, networking, and imaging by providing reconfigurability in computer interconnect topologies, new optical information processing capabilities, optical network switching, and image processing. Depending on the materials and the nanostructures employed in the nanophotonic metastructure devices, various tuning mechanisms can be employed. They include thermo-optical, electro-optical (e.g. Pockels and Kerr effects), magneto-optical, ionic-optical, piezo-optical, mechano-optical (deformation in MEMS or NEMS), and phase-change mechanisms. Such mechanisms can alter the real and/or imaginary parts of the optical susceptibility tensors, leading to tuning of the optical characteristics. In particular, tunable nanophotonic metastructures with relatively large tuning strengths (e.g. large changes in the refractive index) can lead to particularly useful device applications. This paper reviews various tunable nanophotonic metastructures’ tuning mechanisms, tuning characteristics, tuning speeds, and non-volatility. Among the reviewed tunable nanophotonic metastructures, some of the phase-change-mechanisms offer relatively large index change magnitude while offering non-volatility. In particular, Ge–Sb–Se–Te (GSST) and vanadium dioxide (VO 2 ) materials are popular for this reason. Mechanically tunable nanophotonic metastructures offer relatively small changes in the optical losses while offering large index changes. Electro-optically tunable nanophotonic metastructures offer relatively fast tuning speeds while achieving relatively small index changes. Thermo-optically tunable nanophotonic metastructures offer nearly zero changes in optical losses while realizing modest changes in optical index at the expense of relatively large power consumption. Magneto-optically tunable nanophotonic metastructures offer non-reciprocal optical index changes that can be induced by changing the magnetic field strengths or directions. Tunable nanophotonic metastructures can find a very wide range of applications including imaging, computing, communications, and sensing. Practical commercial deployments of these technologies will require scalable, repeatable, and high-yield manufacturing. Most of these technology demonstrations required specialized nanofabrication tools such as e-beam lithography on relatively small fractional areas of semiconductor wafers, however, with advanced CMOS fabrication and heterogeneous integration techniques deployed for photonics, scalable and practical wafer-scale fabrication of tunable nanophotonic metastructures should be on the horizon, driven by strong interests from multiple application areas.

Conventional meta-atom designs rely heavily on researchers’ prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by generative adversarial networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameters requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory. By learning the Markov process that transforms the original structure into a Gaussian distribution, the proposed method can gradually remove the noise starting from the Gaussian distribution and generate new high-degree-of-freedom meta-atoms that meet S-parameters conditions, which avoids the model instability introduced by the adversarial training process of GANs and ensures more accurate and high-quality generation results. Experiments have proven that our method is superior to representative methods of GANs in terms of model convergence speed, generation accuracy, and quality.

State-of-the-art deep learning models can converse and interact with humans by understanding their emotions, but the exponential increase in model parameters has triggered an unprecedented demand for fast and low-power computing. Here, we propose a microcomb-enabled integrated optical neural network (MIONN) to perform the intelligent task of human emotion recognition at the speed of light and with low power consumption. Large-scale tensor data can be independently encoded in dozens of frequency channels generated by the on-chip microcomb and computed in parallel when flowing through the microring weight bank. To validate the proposed MIONN, we fabricated proof-of-concept chips and a prototype photonic-electronic artificial intelligence (AI) computing engine with a potential throughput up to 51.2 TOPS (tera-operations per second). We developed automatic feedback control procedures to ensure the stability and 8 bits weighting precision of the MIONN. The MIONN has successfully recognized six basic human emotions, and achieved 78.5 % accuracy on the blind test set. The proposed MIONN provides a high-speed and energy-efficient neuromorphic computing hardware for deep learning models with emotional interaction capabilities.

Developing methods to sense local variations in properties of nearby materials, such as their refractive index and thickness, are important in numerous fields including chemistry and biomedical applications. Localized surface plasmons (LSPs) excited in plasmonic nanostructures have been demonstrated to be useful in this context due to the spectral location of their associated resonances being sensitive to changes in the environment near the plasmonic structures. This manuscript explores Babinet’s principle by exploiting LSP resonances excited in complementary metal-dielectric cylindrical plasmonic structures (plasmonic particle-dimers and aperture-dimers in our case). Both plasmonic structures are evaluated numerically and experimentally using electron energy loss spectroscopy (EELS), providing a full physical understanding of the complementary nature of the excited LSP resonances. These plasmonic structures are then exploited for dielectric sensing under two configurations: when a thin dielectric film is positioned atop the plasmonic structures and when the analyte surrounds/fills the plasmonic particles/apertures. The complementary sensing performance of both proposed structures is also evaluated, showing the approximate validity of the Babinet principle with sensitivity values of up to ∼650 nm/RIU for thin dielectric sensing.

Both the nonreciprocal surface modes in Weyl semimetal (WSM) with a large anomalous Hall effect and the nonreciprocal photon occupation number on a graphene surface induced by the drift current provide a promising way to manipulate the nonreciprocal near-field energy transfer. Interestingly, the interactions between nonreciprocities are highly important for research in (thermal) photonics but remain challenging. In this study, we theoretically investigated the near-field radiative heat flux transfer between a graphene heterostructure supported by a magnetic WSM and a twist-Weyl semimetal (T-WSM). The nonreciprocal surface mode could be changed by the separation space between two Weyl nodes and the twist angle. Notably, we found that in the absence of a temperature difference between two parallel plates, nonequilibrium fluctuations caused by drift currents led to the transfer of near-field radiative heat flux. Furthermore, these nonreciprocal surface modes interacted with the nonreciprocal photon occupation number in graphene to achieve flexible manipulation of the near-field heat flux size and direction. Additionally, graphene adjustable flux in the case of a temperature difference between the two plates was also discussed. Our scheme can provide a reference for near-field heat flux regulation in nonequilibrium systems.

Herein, the radiation patterns of single-split ring resonators (SSRRs) and double-split ring resonators (DSRRs) in the vertical direction are tailored by reconfiguring the resonator geometries. To design unequal arm lengths for controlling the floating split angle of the resonators and changing their electromagnetic multipole compositions, vertical metamaterials were fabricated using the metal-stress-driven self-folding method. The simulation results well agree with the experimental transmittance and reflectance results and demonstrate the geometry-dependent angle variation of the far-field radiation. Symmetric SSRRs and DSRRs radiate in the vertical and horizontal directions, respectively. With increasing pad shift, the radiation angle of the asymmetric SSRR completely rotates toward the horizontal direction along the ring plane, but the DSRRs can rotate only from 0° to 45° to the horizontal plane. Furthermore, by decomposing the multipoles into their constituents, we show that the directional scattering performance can be verified by manipulating the horizontal and vertical components of the electric dipoles. This novel combination of SSRRs and DSRRs can effectively and efficiently reconfigure the radiation direction in the infrared (IR) region, paving the way for color routers, metasurfaces, and directive IR emitters in compact optical metadevices.

Plasmonic nanocavities form very robust sub-nanometer gaps between nanometallic structures and confine light within deep subwavelength volumes to enable unprecedented control of light–matter interactions. However, spherical nanoparticles acquire various polyhedral shapes during their synthesis, which has a significant impact in controlling many light–matter interactions, such as photocatalytic reactions. Here, we focus on nanoparticle-on-mirror nanocavities built from three polyhedral nanoparticles (cuboctahedron, rhombicuboctahedron, decahedron) that commonly occur during the synthesis. Their photonic modes have a very intricate and rich optical behaviour, both in the near- and far-field. Through a recombination technique, we obtain the total far-field produced by a molecule placed within these nanocavities, to reveal how energy couples in and out of the system. This work paves the way towards understanding and controlling light–matter interactions, such as photocatalytic reactions and non-linear vibrational pumping, in such extreme environments.

Dynamic manipulation of terahertz (THz) beams plays an important role in THz application systems. The PB metasurface provides an effective scheme for space separation and deflection of the spin beam. However, mirror symmetry locking of the conjugated spin states severely limits the versatility of the device. In this work, we demonstrate a liquid crystal (LC) cascaded bilayer metasurface that includes an LC layer, anisotropic metasurface, and PB metasurface. By controlling anisotropy and polarization conversion effects, dynamic spin asymmetric transmission is realized. Meanwhile, two different dynamic energy distribution processes are realized between the L and R state with the corresponding deflection side. The results show that the device achieves a large angular spatial dispersion within the frequency-angle scanning range of ±35° to ±75° corresponding to the broadband range of 0.6–1.1 THz. Moreover, it achieves a spin beam spatial separation with a maximum proportion of energy distribution greater than 26 dB, and the active modulation rate in the energy distribution process reaches 98 %. This work provides a dynamic THz spin conversion and efficient large-angle beam scanning, with important potentials in wavelength/polarization division multiplexing and frequency-scanning antenna for large-capacity THz wireless communication, radar, and imaging systems.

Polarization ( P ), angular index ( l ), and radius index ( p ) are three independent degrees of freedom (DoFs) of vector vortex beams, which have found extensive applications in various domains. While efficient sorting of a single DoF has been achieved successfully, simultaneous sorting of all these DoFs in a compact and efficient manner remains a challenge. In this study, we propose a beam sorter that simultaneously handles all the three DoFs using a diffractive deep neural network (D 2 NN), and demonstrate the robust sorting of 120 Laguerre–Gaussian (LG) modes experimentally in the visible spectrum. Our proposed beam sorter underscores the considerable potential of D 2 NN in optical field manipulation and promises to enhance the diverse applications of vector vortex beams.

Infrared metasurfaces have exhibited exceptional optical properties that differ from naturally occurring metallic and dielectric nanostructure, enabling non-destructive and label-free sensing in a broadband region. However, implementing wavelength multiplexing sensors in broadband infrared has faced significant challenges. These challenges arise from the difficulty in efficiently exciting high Q resonances at specific wavelengths and the inability to individually tune each resonance. Herein, we present a dual resonant metasurface that utilizes a metal–dielectric–metal plasmonic grating and a dielectric–metal channel. By adjusting the vertical and horizontal structures of metasurface, we can independently modify the spectrum of the metasurface in the near-infrared and mid-infrared regions. This broadband infrared metasurface exhibits robust spectral regulation, enabling a polarization-dependent strategy for the dual-resonance. It offers a competitive advantage over traditional metallic nanostructure in refractive index sensing at the second near-infrared window and ultrasensitive vibrational spectroscopy in mid-infrared. Specifically, our proposed metasurface achieves protein concentration sensing and dynamic monitoring of protein concentration in the infrared two-zone. Additionally, it enhances the mid-infrared absorption of amide II with a high Q resonance. The metasurface which combines wavelength multiplexing and polarization dependent switch for protein recognition and trace detection, presents a novel approach for developing high-performance sensors and Integrated photonics sensors in the broadband infrared region.