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A simple approach to acquire wide-band/angle blazing operation in 1-D metallic gratings is investigated by means of mode-matching and equivalent circuit analysis. The gratings are single-groove perfectly conducting gratings that support two propagating guided modes (opposed to typical single-mode cases). It is shown under what conditions one can achieve blazing over a wide range of frequencies and angles. Most importantly, it is identified what governs blazing, in particular the true matching condition that needs to be satisfied, and how the structures’ multiple resonances play role in achieving such matching and tailoring the bandwidth. Parameters of the proposed equivalent circuit model for the case of the transverse magnetic (TM) polarization are analytically derived. The accuracy of the models is verified through a comparison against the results of full-wave simulations. The procedure to achieve wide-band/angle blazing performance is delineated, and the design parameters are explicitly given. It is shown that this structure can strongly transfer the power of the TM polarized incident wave to the −1st diffraction order with a fractional bandwidth of ≈50% at working frequency $f_{0}=10$ GHz and for −10 dB specular reflection loss, thus realizing a broadband structure. From the circuit model, an insightful discussion is presented at various mechanisms at play when Bragg and off-Bragg blazing occurs and fully justifies the behavior of the device. The proposed technique opens up new vistas in a wide range of applications, such as spectroscopy, Littrow cavities, beam splitters, refractive index biosensors, and frequency scanned antenna reflectors.

We report on the first realization of the Hatano-Nelson model in photonics by using an array of active unidirectional resonators that exhibit asymmetric nearest neighbor couplings. Non-Hermitian skin effect and laser phase locking is observed.

We present a dynamic metasurface platform by incorporating the phase-change alloy Ge2Sb2Te5 (GST) into metal-dielectric meta-atoms for active and non-volatile tuning of the optical response. We systematically design a unit cell, which selectively controls the fundamental plasmonic-photonic resonances of the metasurface via the dynamic change of the GST crystalline state. As a proof-of-concept, we experimentally demonstrate miniaturized tunable metasurfaces that globally manipulate amplitude and phase of incident light necessary for near-perfect absorption and anomalous/specular beam deflection, respectively. Our findings further substantiate reconfigurable hybrid metasurfaces as promising candidates for the development of miniaturized energy harvesting and optical signal processing devices.

We demonstrate a new platform for reconfigurable third-order nonlinear photonic devices formed by silicon dioxide (SiO2)-Sb2S3(Sb2Se3)-SiO2 subwavelength Fabry-Perot cavities on a gold (Au) reflector, which exhibit giant third-harmonic generation (THG) modulations with enhanced efficiency. The use of the phase-change dichalcogenides (Sb2S3 or Sb2Se3) enables a wide tuning range of the THG response. The devices work at dispersion-engineered THG resonances at the crystalline phase (c-phase) of the PCC, which numerically exhibit c-phase THG flows a few 100 times more than those at the amorphous phase (a-phase) of the PCC at near-infrared excitation wavelengths.

Efficient hybrid plasmonic-photonic metasurfaces that simultaneously take advantage of the potential of both pure metallic and all-dielectric nanoantennas are identified as an emerging technology in flat optics. Nevertheless, post-fabrication tunable hybrid metasurfaces are still elusive. Here, we present a reconfigurable hybrid metasurface platform by incorporating the phase-change material Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST) into metal-dielectric meta-atoms for active and non-volatile tuning of properties of light. We systematically design a reduced-dimension meta-atom, which selectively controls the fundamental hybrid plasmonic-photonic resonances of the metasurface via the dynamic change of optical constants of GST without compromising the scattering efficiency. As a proof-of-concept, we experimentally demonstrate miniaturized tunable metasurfaces that control the amplitude and phase of incident light necessary for high-contrast optical switching and anomalous to specular beam deflection, respectively. Finally, we leverage a deep learning-based approach to present an intuitive low-dimensional visualization of the enhanced range of response reconfiguration enabled by the addition of GST. Our findings further substantiate dynamically tunable hybrid metasurfaces as promising candidates for the development of small-footprint energy harvesting, imaging, and optical signal processing devices.

We experimentally demonstrate active modulation of amplitude/phase profiles of optical wavefronts by leveraging the interplay of surface plasmon polariton and electric/magnetic Mie resonance modes in hybrid plasmonic-dielectric metasurface platforms incorporating chalcogenide phase-change materials.

We experimentally demonstrate a tunable hybrid metasurface benefiting from phase-change materials and plasmon hybridization for non-volatile optical modulation. We also leverage machine learning algorithms to study the effect of structural parameters on the optical performance.

Here, we leverage Mie scattering resonances supported by an all-dielectric metasurface made of phase-change material GeSe3 nanopillars to demonstrate nanoscale high-saturation color switching.

An electrically-tunable all-dielectric metasurface employing a phase-change material, GST, and transparent conductive indium tin oxide (ITO) is designed and experimentally demonstrated for controlling light absorption to enable multi-level electro-optic modulation with unprecedented sensitivity.

In contrast to lossy plasmonic metasurfaces (MSs), wideband dielectric MSs comprising subwavelength nanostructures supporting Mie resonances are of great interest in the visible wavelength range. Here, for the first time to our knowledge, we experimentally demonstrate a reflective MS consisting of a square-lattice array of hafnia (HfO2) nanopillars to generate a wide color gamut. To design and optimize these MSs, we use a deep-learning algorithm based on a dimensionality reduction technique. Good agreement is observed between simulation and experimental results in yielding vivid and high-quality colors. We envision that these structures not only empower the high-resolution digital displays and sensitive colorimetric biosensors but also can be applied to on-demand applications of beaming in a wide wavelength range down to deep ultraviolet.

International audience; 1-D compound metallic grating (CMG) is a periodic structure with more than one slit in each period. When CMG is combined with a graphene sheet as its cover, the incident light is effectively coupled to the plasmons in graphene which in turn can result in strong manipulation of light for both major polarizations. We show that tunable phase resonance and perfect absorption of the incident light are interesting outcomes of this manipulation. In this paper, we demonstrate that fano-like phase resonances which can be observed in CMGs under transverse magnetic polarized incident wave are tuned by changing the Fermi level of graphene. It is shown that while the spectral position of the phase resonances can be shifted up to several gigahertzes, their peak to peak amplitudes are tuned from ~0.9 to ~0.1. On the other hand, we design a graphene-covered CMG, which is able to perfectly absorb both major polarizations of incident wave in two separate bands; hence, providing the opportunity for designing multi-band/wide-band absorbers. We have developed a circuit model for the analysis of the structure. Parameters of the model are derived explicitly and analytically for both major polarizations. Our results are verified through comparison against results of the full-wave simulations.

We demonstrate an electro-optically reconfigurable hybrid metasurface platform formed by integration of plasmonic nanostructures with phase-change materials to effectively modulate the incident light over a broad range of wavelengths in the telecommunication window.

We experimentally demonstrate reconfigurable metasruface capable of high modulating of scattered light thanks to the interplay of electric and magnetic Mie resonance modes, due to the induced intermediate states of GST necessary for beaming applications.

Leveraging sharp toroidal resonances supported by an all-dielectric metasurface (MS) made of high-index phase-change material Sb2S3 nanopillars (NPs), we demonstrate tunable ultrahighly saturated structural color with unprecendted large color gamut.

Herein, for the first time to our knowledge, we experimentally demonstrate Fano resonant all-dielectric metasurfaces comprising of high- and median-index nanopillars (HfO2, TiO2 and ZrO2) with zero loss in visible range for polarization-sensitive structural coloration.

We present an electrically tunable metasurface as an optical phased array antenna by employing a deep learning approach to intelligently design hybrid phase- change/plasmonic materials-based meta-atoms spatially adding 0 and n phase-shift while minimizing reflectance variation.

We present a learning-based technique to develop a reliable tool for discovering the underlying physics and feasibility of achieving different type of responses in electromagnetic nanostructures.

We demonstrate a reconfigurable all-dielectric metasurface comprising of concentric phase-change/high-index materials nanopillars. The dynamic interplay of dominant Mie resonance modes due to the real-time phase-transition of Ge2Sb2Te5 grants 2n phase agility with unprecedented efficiency.

Here, we experimentally demonstrate a polarization-independent metasurface (MS) in the near-infrared regime by employing a hybrid plasmonicfphase-change material architecture for non-volatile and wide-band tunable modulation.

Here, we experimentally demonstrate full color generation by a Fano-resonant all-dielectric metasurface (MS) consisting of HfO2 nanopillars (NPs), for the first time to our knowledge, designed by a novel deep learning approach named dimensionality reduction.

Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass-producible, cost-effective, and efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multipurpose functionalities, chalcogenide phase-change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non-volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long-term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state-of-the-art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, and thermo-optic effects of well-established classes of chalcogenide PCMs. The emerging deep learning-based approaches for the optimization of reconfigurable MSs and the analysis of light-matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area.

We numerically demonstrate a dynamically tunable all-dielectric metalens based on low-loss optical phase-change chalcogenide antimony sulfide (Sb2S3) to co-focus fundamental and third-harmonic generation (THG) signals with high dynamic range.

We present a new approach based on machine learning algorithms for inverse design of photonic nanostructure to provide the desired response while iteratively reducing the complexity of the structure to minimize the design complexity.

In contrast to lossy plasmonic metasurfaces (MSs), wideband dielectric MSs comprising subwavelength nanostructures supporting Mie resonances are of great interest in the visible wavelength range. Here, for the first time to our knowledge, we experimentally demonstrate a reflective MS consisting of a square-lattice array of hafnia (HfO

In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing significant error. Such an approach implicitly provides useful information about the role of different features (or design parameters such as geometry) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, we apply it to a reconfigurable optical metadevice enabling dual-band and triple-band optical absorption in the telecommunication window. Combination of the proposed approach with existing commercialized full-wave simulation tools offers a powerful toolkit to extract basic mechanisms of wave-matter interaction in complex EM devices and facilitate the design and optimization of nanostructures for a large range of applications including imaging, spectroscopy, and signal processing. It is worth to mention that the demonstrated approach is general and can be used in a large range of problems as long as enough training data can be provided.

Inducing a large refractive-index change is the holy grail of reconfigurable photonic structures, a goal that has long been the driving force behind the discovery of new optical material platforms. Recently, the unprecedentedly large refractive-index contrast between the amorphous and crystalline states of Ge-Sb-Te (GST)-based phase-change materials (PCMs) has attracted tremendous attention for reconfigurable integrated nanophotonics. Here, we introduce a microheater platform that employs optically transparent and electrically conductive indium-tin-oxide (ITO) bridges for the fast and reversible electrical switching of the GST phase between crystalline and amorphous states. By the proper assignment of electrical pulses applied to the ITO microheater, we show that our platform allows for the registration of virtually any intermediate crystalline state into the GST film integrated on the top of the designed microheaters. More importantly, we demonstrate the full reversibility of the GST phase between amorphous and crystalline states. To show the feasibility of using this hybrid GST/ITO platform for miniaturized integrated nanophotonic structures, we integrate our designed microheaters into the arms of a Mach-Zehnder interferometer to realize electrically reconfigurable optical phase shifters with orders of magnitude smaller footprints compared to existing integrated photonic architectures. We show that the phase of optical signals can be gradually shifted in multiple intermediate states using a structure that can potentially be smaller than a single wavelength. We believe that our study showcases the possibility of forming a whole new class of miniaturized reconfigurable integrated nanophotonics using beyond-binary reconfiguration of optical functionalities in hybrid PCM-photonic devices.

The collective response of a system is profoundly shaped by the interaction dynamics between its constituent elements. In physics, tailoring these interactions can enable the observation of unusual phenomena that are otherwise inaccessible in standard settings, ranging from the possibility of a Kramer's degeneracy even in the absence of spin to the breakdown of the bulkboundary correspondence. Here, we show how such tailored asymmetric coupling terms can be realized in photonic integrated platforms by exploiting non-Hermitian concepts. In this regard, we introduce a generalized photonic molecule composed of a pair of microring resonators with internal S-bends connected via two directional couplers and a link waveguide. By judiciously designing the parameters of this system, namely the length of the links and the power division ratio of the directional couplers, we experimentally show the emergence of Hermitian and non-Hermitian type exchange interactions. The ramifications of such coupling dynamics are then studied in 1D chain and ring-type active lattices. Our findings establish the proposed structure as a promising building block for the realization of a variety of phenomena, especially those associated with phase locking in laser arrays and non-Hermitian topological lattices.

We present here a new approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than device optimization in electromagnetic (EM) nanostructures. This approach uses training data obtained through full-wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of EM nanostructures. To facilitate the knowledge discovery and reduce the computation complexity, our approach combines the dimensionality reduction technique (using an autoencoder) with convex-hull and one-class support-vector-machine (SVM) algorithms to find the range of the feasible responses in the latent (or the reduced) response space of the EM nanostructure. We show that by using a small set of training instances (compared to all possible structures), our approach can provide better than 95% accuracy in assessing the feasibility of a given response. More importantly, the one-class SVM algorithm can be trained to provide the degree of feasibility (or unfeasibility) of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of our approach, we apply it to two important classes of binary metasurfaces (MSs), formed by array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. In addition to theoretical results, we show the experimental results obtained by fabricating several MSs of the second class. Our theoretical and experimental results confirm the unique features of this approach for knowledge discovery in EM nanostructures., Comment: Adv. Intell. Syst. (2020)

Here, we leverage a machine learning technique as an efficient alternative approach to traditional optimization techniques to significantly reduce the computation cost of the forward design problem in the optimization process of optical devices.

Leveraging sharp Fano-resonances supported by an all-dielectric metasurface (MS) fabricated from high-index HfO2 nanopillars (NPs), with zero loss in the visible range, we demonstrate a high contrast structural color with wide color gamut map.

A hybrid plasmonic-dielectric metasurface is proposed in order to manipulate beam propagation in desired manners. The metasurface is composed of patterned hybrid graphene-silicon nano-disks deposited on a low-index substrate, namely silica. It is shown that the proposed hybrid metasurface simultaneously benefits from the advantages of graphene-based metasurfaces and dielectric ones. Specially, we show that the proposed hybrid metasurface not only provides reconfigurability, just like previously proposed graphene-based metasurfaces, but also similar to dielectric metasurfaces, is of low loss and CMOS-compatible. Such exceptional features give the metasurface exceptional potentials to realize high efficient optical components. To demonstrate the latter point, focusing and anomalous reflection are performed making use of the proposed hybrid structure as examples of two well-known optical functionalities. This work opens up a new route in realization of reconfigurable meta-devices with widely real-world applications which cannot be achieved with their passive counterparts.

We present a novel approach based on dimensionality reduction for designing electromagnetic nanostructures. The method relies on reducing the dimensionality of the input/output space to reduce the complexity of forward and inverse design problems.