Evolutionary optimization of optical antennas

Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

A chiral inverse Faraday effect mediated by an inversely designed plasmonic antenna

The inverse Faraday effect is a magneto-optical process allowing the magnetization of matter by an optical excitation carrying a non-zero spin of light. This phenomenon was considered until now as symmetric; right or left circular polarizations generate magnetic fields oriented in the direction of light propagation or in the counter-propagating direction. Here, we demonstrate that by manipulating the spin density of light in a plasmonic nanostructure, we generate a chiral inverse Faraday effect, creating a strong magnetic field of 500 mT only for one helicity of the light, the opposite helicity producing this effect only for the mirror structure. This new optical concept opens the way to the generation of magnetic fields with unpolarized light, finding application in the ultrafast manipulation of magnetic domains and processes, such as spin precession, spin currents and waves, magnetic skyrmion or magnetic circular dichroism, with direct applications in data storage and data processing technologies.

Computational Investigation of Advanced Refractive Index Sensor Using 3-Dimensional Metamaterial Based Nanoantenna Array

Photonic researchers are increasingly exploiting nanotechnology due to the development of numerous prevalent nanosized manufacturing technologies, which has enabled novel shape-optimized nanostructures to be manufactured and investigated. Hybrid nanostructures that integrate dielectric resonators with plasmonic nanostructures are also offering new opportunities. In this work, we have explored a hybrid coupled nano-structured antenna with stacked multilayer lithium tantalate (LiTaO₃) and Aluminum oxide (Al₂O₃), operating at wavelength ranging from 400 nm to 2000 nm. Here, the sensitivity response has been explored of these nano-structured hybrid arrays. It shows a strong electromagnetic confinement in the separation gap (g) of the dimers due to strong surface plasmon resonance (SPR). The influences of the structural dimensions have been investigated to optimize the sensitivity. The designed hybrid coupled nanostructure with the combination of 10 layers of gold (Au) and Lithium tantalate (LiTaO₃) or Aluminum oxide (Al₂O₃) (five layers each) having height, h₁ = h₂ = 10 nm exhibits 730 and 660 nm/RIU sensitivity, respectively. The sensitivity of the proposed hybrid nanostructure has been compared with a single metallic (only gold) elliptical paired nanostructure. Depending on these findings, we demonstrated that a roughly two-fold increase in the sensitivity (S) can be obtained by utilizing a hybrid coupled nanostructure compared to an identical nanostructure, which competes with traditional sensors of the same height, (h). Our innovative novel plasmonic hybrid nanostructures provide a framework for developing plasmonic nanostructures for use in various sensing applications.

Fast Topology Optimization for Near-Field Focusing All-Dielectric Metasurfaces Using the Discrete Dipole Approximation

Using an efficient implementation of the discrete dipole approximation and topology optimization, we design all-dielectric metasurfaces capable of focusing light into intense deep subwavelength hotspots. The light focusing of these metasurfaces far outweighs conventional lenses and can provide dramatic enhancements of processes that depend superlinearly on light intensity, such as light-powered membrane distillation and photocatalysis. Our approach can easily be generalized to optimize metasurfaces for other functionalities, such as nonlinear optics or photothermal conversion.

Magnetic and electric Purcell factor control through geometry optimization of high index dielectric nanostructures

We design planar silicon antennas for controlling the emission rate of magnetic or electric dipolar emitters. Evolutionary algorithms coupled to the Green Dyadic Method lead to different optimized geometries which depend on the nature and orientation of the dipoles. We discuss the physical origin of the obtained configurations thanks to modal analysis but also emphasize the role of nanoscale design of the LDOS. We complete our study using finite element method and demonstrate an enhancement up to 2 × 10³ of the magnetic Purcell factor in europium ions. Our work brings together random optimizations to explore geometric parameters without constraint, a first order deterministic approach to understand the optimized designs and a modal analysis which clarifies the physical origin of the exaltation of the magnetic Purcell effect.

Metamaterial Design with Nested-CNN and Prediction Improvement with Imputation

Metamaterials, which are not found in nature, are used to increase the performance of antennas with their extraordinary electromagnetic properties. Since metamaterials provide unique advantages, performance improvements have been made with many optimization algorithms. Objective: The article aimed to develop a deep learning model that, unlike traditional optimization algorithms, takes the desired reflection coefficients’ parameter as an input and gives the image of the corresponding metamaterial. Method: An amount of 29,722 metamaterial images and reflection coefficients corresponding to the metamaterials were collected. Nested-CNN, designed for this task, consisted of Model-1 and Model-2. Model-1 was designed to generate the shape of metamaterial with a reflection coefficient as the input. Model-2 was designed to detect the reflection coefficient of a given image of metamaterial input. Created by using Model-2 in Model-1’s loss function, the nested-CNN was updated by comparing the reflection coefficient of the produced image with the desired reflection coefficient. Secondly, imputation, which is usually the complete missing data before the process of training in machine learning algorithms, was proposed to use in the prediction side to improve the performance of the nested-CNN. The imputation for prediction was used for the non-interested part of the reflection coefficient to decrease the error of the interested region of the reflection coefficient. In the experiment, 27,222 data were used for the KNN-imputer, half of the reflection coefficient was considered as the non-interested region. Additionally, 40 neighbors and 50 neighbors were given the best mean absolute errors (MAE) for specified conditions. Result: The given results are based on test data. For Model-2, the MAE was 0.27, the R2 score was 0.96, and the mean correlation coefficient was 0.93. The R2 score for the nested-CNN was 0.9, the MAE of nested-CNN was 0.42, and the MAE of nested-CNN with 50 neighbors was 0.17.

Tesla-Range Femtosecond Pulses of Stationary Magnetic Field, Optically Generated at the Nanoscale in a Plasmonic Antenna

The inverse Faraday effect allows the generation of stationary magnetic fields through optical excitation only. This light-matter interaction in metals results from creating drift currents via nonlinear forces that light applies to the conduction electrons. Here, we describe the theory underlying the generation of drift currents in metals, particularly its application to photonic nanostructures using numerical simulations. We demonstrate that a gold photonic nanoantenna, optimized by a genetic algorithm, allows, under high excitation power, to maximize the drift currents and generate a pulse of stationary magnetic fields in the tesla range. This intense magnetic field, confined at the nanoscale and for a few femtoseconds, results from annular optical confinement and not from the creation of a single optical hot spot. Moreover, by controlling the incident polarization state, we demonstrate the orientation control of the created magnetic field and its reversal on demand. Finally, the stationary magnetic field's temporal behavior and the drift currents associated with it reveal the subcycle nature of this light-matter interaction. The manipulation of drift currents by a plasmonic nanostructure for the generation of stationary magnetic field pulses finds applications in the ultrafast control of magnetic domains with applications not only in data storage technologies but also in research fields such as magnetic trapping, magnetic skyrmion, magnetic circular dichroism, to spin control, spin precession, spin currents, and spin-waves, among others.

Metamaterial perfect absorber with morphology-engineered meta-atoms using deep learning

Metamaterial perfect absorbers (MPAs) typically have regularly-shaped unit structures owing to constraints on conventional analysis methods, limiting their absorption properties. We propose an MPA structure with a general polygon-shaped meta-atom. Its irregular unit structure provides multiple degrees-of-freedom, enabling flexible properties, such as dual-band absorption. We constructed a deep neural network to predict the parameters of the corresponding MPA structure with a given absorptivity as input, and vice versa. The mean-square error was as low as 0.0017 on the validation set. This study provides a basis for the design of complicated artificial electromagnetic structures for application in metamaterials and metasurfaces.

Transparent ultra-wideband double-resonance-layer metamaterial absorber designed by a semiempirical optimization method

In this study, a transparent ultra-wideband double-resonance-layer absorber was designed using a semiempirical optimization method. In this method, an equivalent circuit model, genetic algorithm, and parameter fitting are employed to reduce the computation time and improve the design flexibility. Simulations and measurements show that the as-designed absorber can achieve ultrawide microwave absorption in the range of 2.00 to 11.37 GHz with a fractional bandwidth of 140.2%. Furthermore, electric field and surface current distributions show that the broad bandwidth was derived from the good matching of the absorption peaks in the two resonance layers. In addition, the target waveband of the as-designed absorber covered the wavebands of WiFi and radio-frequency identification, as well as part of the 5G waveband. This makes the proposed absorber a good candidate for daily electromagnetic pollution reduction.

Numerical Analysis of MIM-Based Log-Spiral Rectennas for Efficient Infrared Energy Harvesting

This work presents the design and analysis of a metal-insulator-metal (MIM)-based optical log spiral rectenna for efficient energy harvesting at 28.3 THz. To maximize the benefits of the enhanced field of the proposed nano-antenna in the rectification process, the proposed design considers the antenna arms (Au) as the electrodes of the rectifying diode and the insulator is placed between the electrode terminals for the compact design of the horizontal MIM rectenna. The rectifier insulator, Al₂O₃, was inserted at the hotspot located in the gap between the antennas. A detailed analysis of the effect of different symmetric and asymmetric MIM-configurations (Au-Al₂O₃-Ag, Au-Al₂O₃-Al, Au-Al₂O₃-Cr, Au-Al₂O₃-Cu, and Au-Al₂O₃-Ti) was conducted. The results of the study suggested that the asymmetric configuration of Au-Al₂O₃-Ag provides optimal results. The proposed design benefits from the captured E-field intensity, I-V, resistivity, and responsivity and results in a rectenna that performs efficiently.

Meta-atom library generation via an efficient multi-objective shape optimization method

Optimizing the shape of metasurface unit cells can lead to tremendous performance gains in several critically important areas. This paper presents a method of generating and optimizing freeform shapes to improve efficiency and achieve multiple metasurface functionalities (e.g., different polarization responses). The designs are generated using a three-dimensional surface contour method, which can produce an extensive range of nearly arbitrary shapes using only a few variables. Unlike gradient-based topology optimization, the proposed method is compatible with existing global optimization techniques that have been shown to significantly outperform local optimization algorithms, especially in complex and multimodal design spaces.

Growth of Au Hollow Stars and Harmonic Excitation Energy Transfer

Optical excitation, subsequent energy transfer, and emission are fundamental to many physical problems. Optical antennas are ideal candidates for manipulating these processes. We extend energy transfer to second- and third-harmonic (SH and TH) fields through the collaborative susceptibility χ⁽ⁿ⁾ (n = 1, 2, 3) resonances of nonlinear optical antennas. Hollow gold stars, with a broadband response covering the fundamental, SH, and TH frequencies, are synthesized as nonlinear antennas. Harmonic resonance energy transfer through a χ⁽³⁾ → χ⁽¹⁾ collaboration is revealed. A χ⁽³⁾ → χ⁽²⁾ collaboration is uncovered, with largely enhanced SH radiation demonstrated by exciting the three resonances at the fundamental, SH, and TH frequencies. A theoretical model of the effective nonlinear susceptibilities is proposed to calculate the efficiencies of the two nonlinear energy transfer processes.

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.

Design of plasmonic directional antennas via evolutionary optimization

We demonstrate inverse design of plasmonic nanoantennas for directional light scattering. Our method is based on a combination of full-field electrodynamical simulations via the Green dyadic method and evolutionary optimization (EO). Without any initial bias, we find that the geometries reproducibly found by EO work on the same principles as radio-frequency antennas. We demonstrate the versatility of our approach by designing various directional optical antennas for different scattering problems. EO-based nanoantenna design has tremendous potential for a multitude of applications like nano-scale information routing and processing or single-molecule spectroscopy. Furthermore, EO can help to derive general design rules and to identify inherent physical limitations for photonic nanoparticles and metasurfaces.

Plasmonic-Nanopore Biosensors for Superior Single-Molecule Detection

Plasmonic and nanopore sensors have separately received much attention for achieving single-molecule precision. A plasmonic "hotspot" confines and enhances optical excitation at the nanometer length scale sufficient to optically detect surface-analyte interactions. A nanopore biosensor actively funnels and threads analytes through a molecular-scale aperture, wherein they are interrogated by electrical or optical means. Recently, solid-state plasmonic and nanopore structures have been integrated within monolithic devices that address fundamental challenges in each of the individual sensing methods and offer complimentary improvements in overall single-molecule sensitivity, detection rates, dwell time and scalability. Here, the physical phenomena and sensing principles of plasmonic and nanopore sensing are summarized to highlight the novel complementarity in dovetailing these techniques for vastly improved single-molecule sensing. A literature review of recent plasmonic nanopore devices is then presented to delineate methods for solid-state fabrication of a range of hybrid device formats, evaluate the progress and challenges in the detection of unlabeled and labeled analyte, and assess the impact and utility of localized plasmonic heating. Finally, future directions and applications inspired by the present state of the art are discussed.

Reversible Mapping and Sorting the Spin of Photons on the Nanoscale: A Spin-Optical Nanodevice

The photon spin is an important resource for quantum information processing as is the electron spin in spintronics. However, for subwavelength confined optical excitations, polarization as a global property of a mode cannot be defined. Here, we show that any polarization state of a plane-wave photon can reversibly be mapped to a pseudospin embodied by the two fundamental modes of a subwavelength plasmonic two-wire transmission line. We design a device in which this pseudospin evolves in a well-defined fashion throughout the device reminiscent of the evolution of photon polarization in a birefringent medium and the behavior of electron spins in the channel of a spin field-effect transistor. The significance of this pseudospin is enriched by the fact that it is subject to spin-orbit locking. Combined with optically active materials to exert external control over the pseudospin precession, our findings could enable spin-optical transistors, that is, the routing and processing of quantum information with light on a subwavelength scale.

Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale

Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.

Pushing the limits of optical information storage using deep learning

Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust readout schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning-based approach in which the scattering spectra are analysed by an artificial neural network, we achieve quasi-error-free readout of sequences of up to 9 bits, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production-ready complementary metal-oxide-semiconductor technology.

Plasmonic nanoantenna-dielectric nanocavity hybrids for ultrahigh local electric field enhancement

A dielectric nanostructure with a high refractive index can exhibit strong optical resonances with considerable electric field enhancement around the entire structure volume. Here we show theoretically that a dielectric structure with this feature can boost the local electric field of a small plasmonic nanoantenna placed nearby. We construct a hybrid system of a plasmonic nanoantenna and a dielectric nanocavity, where the nanocavity is a concentric disk-ring structure with a lossless material n = 3.3 and the nanoantenna is a gold nanorod dimer. The resonant electric field enhancement at the gap center of the antenna in the hybrid structure reaches more than one order of magnitude higher than that of the individual antenna. The dielectric structure plays two roles in the hybrid system, namely the amplified excitation field and an environment causing the redshift of the antenna resonance. The hybrid configuration is applicable to the cases with various geometries and different materials of the hybrid system. Our results can find applications in enhanced nanoscale light-matter interactions such as surface-enhanced Raman scattering, nonlinear optics, and plasmon-exciton couplings.

Nanophotonics with 2D transition metal dichalcogenides [Invited]

Two-dimensional semiconducting transition metal dichalcogenides (TMDCs) have recently become attractive materials for several optoelectronic applications, such as photodetection, light harvesting, phototransistors, light-emitting diodes, and lasers. Their bandgap lies in the visible and near-IR range, and they possess strong excitonic resonances, high oscillator strengths, and valley-selective response. Coupling these materials to optical nanocavities enhances the quantum yield of exciton emission, enabling advanced quantum optics and nanophotonics devices. Here, we review the state-of-the-art advances of hybrid exciton-polariton structures based on monolayer TMDCs coupled to plasmonic and dielectric nanocavities. We discuss the optical properties of 2D WS₂, WSe₂, MoS₂ and MoSe₂ materials, paying special attention to their energy bands, photoluminescence/absorption spectra, excitonic fine structure, and to the dynamics of exciton formation and valley depolarization. We also discuss light-matter interactions in such hybrid exciton-polariton structures. Finally, we focus on weak and strong coupling regimes in monolayer TMDCs-based exciton-polariton systems, envisioning research directions and future opportunities for this material platform.

Freeform engineered disordered metalenses for super-resolution imaging and communication

Effective transmission of information through scattering media has been of great importance in imaging systems and beneficial to high capacity wireless communication. Despite numerous attempts to achieve high-resolution sub-diffraction-limited imaging through employing the engineered structures such as the so-called metamaterials or utilizing techniques like time reversal methods, the proposed ideas suffer from the fundamental limitations for design and practical realization. In this paper, we investigate disorder-based engineered scattering structures and introduce a novel technique for achieving super-resolution based on designing and employing engineered all-dielectric medium. We show that disorder in the proposed design can be exploited to significantly modify the information content of scattered fields in the far-field region. Under the presence of the designed structures, using computational methods, signals associated with ultra sub-wavelength features of the illuminating sources can be enhanced and extracted from the far-field image. Not only can the presented approach lead to remarkable enhancement of resolution in such systems, but also orthogonal transmission channels are attainable when the closely-packed sources are excited properly. The latter provides a new scheme for encoding and multiplexing signals leading to the enhancement of information capacity in emerging information processing systems. The design procedure and physical constraints are studied and discussed.

Optimization of Ag coated hydrogen silsesquioxane square array hybrid structure design for surface-enhanced Raman scattering substrate

A computer-automated design process for a surface-enhanced Raman scattering (SERS) substrate using a particle swarm optimization algorithm is proposed. Nanostructured Ag coated hydrogen silsesquioxane nanopillar arrays of various sizes for SERS substrate applications are fabricated by direct Ag film deposition on substrates patterned by electron beam lithography and are investigated systematically. Good agreement is demonstrated between experimental and simulation results. The absorption spectra, charge distributions, and electric field distributions are calculated using finite-difference time-domain simulations to explain the field enhancement mechanism and indicate that this enhancement originates from plasmon resonance. Our work provides a guide towards optimum SERS substrate design.

Plasmonic nanostructure design and characterization via Deep Learning

Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

Mode Matching for Optical Antennas

The emission rate of a point dipole can be strongly increased in the presence of a well-designed optical antenna. Yet, optical antenna design is largely based on radio-frequency rules, ignoring, e.g., Ohmic losses and non-negligible field penetration in metals at optical frequencies. Here, we combine reciprocity and Poynting's theorem to derive a set of optical-frequency antenna design rules for benchmarking and optimizing the performance of optical antennas driven by single quantum emitters. Based on these findings a novel plasmonic cavity antenna design is presented exhibiting a considerably improved performance compared to a reference two-wire antenna. Our work will be useful for the design of high-performance optical antennas and nanoresonators for diverse applications ranging from quantum optics to antenna-enhanced single-emitter spectroscopy and sensing.

Plasmonic nanoantenna design and fabrication based on evolutionary optimization

Nanoantennas can tailor light-matter interaction for optical communication, sensing, and spectroscopy. Their design is inspired by radio-frequency rules which partly break down at optical frequencies. Here we find unexpected nanoantenna designs exhibiting strong light localization and enhancement by using a general and scalable evolutionary algorithm based on FDTD simulations that also accounts for geometrical fabrication constraints. The resulting nanoantennas are "printed" directly by focused-ion beam milling and their fitness ranking is validated experimentally by two-photon photoluminescence. We find the best antennas' operation principle deviating from that of classical radio wave-inspired designs. Our work sets the stage for a widespread application of evolutionary optimization in nano photonics.

Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas

The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. Here, we present a numerical technique that can allow the design of photonic nanostructures with optical properties optimized along several arbitrary objectives. In particular, we combine evolutionary multi-objective algorithms with frequency-domain electrodynamical simulations to optimize the design of colour pixels based on silicon nanostructures that resonate at two user-defined, polarization-dependent wavelengths. The scattering spectra of optimized pixels fabricated by electron-beam lithography show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints and therefore particularly apt for the design of complex structures within predefined technological limits.

Etchant-based design of gold tip apexes for plasmon-enhanced Raman spectromicroscopy

In this paper, we gain insight into the design and optimization of plasmonic (metallic) tips prepared with dc-pulsed voltage electrochemical etching gold wires, provided that, a duty cycle is self-tuned. Physically, it means that etching electrolyte attacks the gold wire equally for all pulse lengths, regardless of its surface shape. Etchant effect on the reproducibility of a curvature radius of the tip apex is demonstrated. It means that the gold conical tips can be designed chemically with a choice of proper etchant electrolyte. It is suggested to use a microtomed binary polymer blend consisting of polyamide and low density polyethylene, as a calibration grating, for optimizing and standardizing tip-enhanced Raman scattering performance.

Topology optimization for three-dimensional electromagnetic waves using an edge element-based finite-element method

This paper develops a topology optimization procedure for three-dimensional electromagnetic waves with an edge element-based finite-element method. In contrast to the two-dimensional case, three-dimensional electromagnetic waves must include an additional divergence-free condition for the field variables. The edge element-based finite-element method is used to both discretize the wave equations and enforce the divergence-free condition. For wave propagation described in terms of the magnetic field in the widely used class of non-magnetic materials, the divergence-free condition is imposed on the magnetic field. This naturally leads to a nodal topology optimization method. When wave propagation is described using the electric field, the divergence-free condition must be imposed on the electric displacement. In this case, the material in the design domain is assumed to be piecewise homogeneous to impose the divergence-free condition on the electric field. This results in an element-wise topology optimization algorithm. The topology optimization problems are regularized using a Helmholtz filter and a threshold projection method and are analysed using a continuous adjoint method. In order to ensure the applicability of the filter in the element-wise topology optimization version, a regularization method is presented to project the nodal into an element-wise physical density variable.

Experimental realization of ultrathin, double-sided metamaterial perfect absorber at terahertz gap through stochastic design process

We design and demonstrate a flexible, ultrathin and double-sided metamaterial perfect absorber (MPA) at 2.39 terahertz (THz), which enables excellent light absorbance under incidences from two opposite sides. Herein, the MPA is fabricated on a λ₀/10.1-thick flexible polyethylene terephthalate substrate of ε_r = 2.75 × (1 + 0.12i), sandwiched by two identical randomized metallic patterns by our stochastic design process. Such an MPA provides tailored permittivity and permeability to approach the impedance of free space for minimizing reflectance and a great imaginary part of the refractive index for reducing transmittance and finally results in high absorbance. Both experimental measurement and numerical simulation are in a good agreement. The flexible, ultrathin and double-sided MPA significantly differs from traditional quarter-wavelength absorbers and other single-sided perfect absorbers, paving a way toward practical THz applications in thermal emission, sensing and imaging, communications, stealth technique, and even energy harvesting.

Ultimate Limit of Light Extinction by Nanophotonic Structures

Nanophotonic structures make it possible to precisely engineer the optical response at deep subwavelength scales. However, a fundamental understanding of the general performance limits remains a challenge. Here we use extensive electrodynamics simulations to demonstrate that the so-called f-sum rule sets a strict upper bound to the light extinction by nanostructures regardless their internal interactions and retardation effects. In particular, we show that the f-sum rule applies to arbitrarily complex plasmonic metal structures that exhibit an extraordinary spectral sensitivity to size, shape, near-field coupling effects, and incident polarization. The results may be used for benchmarking light scattering and absorption efficiencies, thus imposing fundamental limits on solar light harvesting, biomedical photonics, and optical communications.

Evolutionary optimization of compact dielectric lens for farfield sub-wavelength imaging

The resolution of conventional optical lenses is limited by diffraction. For decades researchers have made various attempts to beat the diffraction limit and realize subwavelength imaging. Here we present the approach to design modified solid immersion lenses that deliver the subwavelength information of objects into the far field, yielding magnified images. The lens is composed of an isotropic dielectric core and anisotropic or isotropic dielectric matching layers. It is designed by combining a transformation optics forward design with an inverse design scheme, where an evolutionary optimization procedure is applied to find the material parameters for the matching layers. Notably, the total radius of the lens is only 2.5 wavelengths and the resolution can reach λ/6. Compared to previous approaches based on the simple discretized approximation of a coordinate transformation design, our method allows for much more precise recovery of the information of objects, especially for those with asymmetric shapes. It allows for the far-field subwavelength imaging at optical frequencies with compact dielectric devices.

Deterministic temperature shaping using plasmonic nanoparticle assemblies

We introduce a deterministic procedure, named TSUNA for Temperature Shaping Using Nanoparticle Assemblies, aimed at generating arbitrary temperature distributions on the microscale. The strategy consists in (i) using an inversion algorithm to determine the exact heat source density necessary to create a desired temperature distribution and (ii) reproducing experimentally this calculated heat source density using smart assemblies of lithographic metal nanoparticles under illumination at their plasmonic resonance wavelength. The feasibility of this approach was demonstrated experimentally by thermal microscopy based on wavefront sensing.

Cooperative electromagnetic interactions between nanoparticles for solar energy harvesting

The cooperative electromagnetic interactions between discrete resonators have been widely used to modify the optical properties of metamaterials. Here we propose a general approach for engineering these interactions both in the dipolar approximation and for any higher-order description. Finally we apply this strategy to design broadband absorbers in the visible range from simple n-ary arrays of metallic nanoparticles.

Design, optimization and fabrication of a 28.3 THz nano-rectenna for infrared detection and rectification

The increasing energy demands of the world's population and the quickly diminishing fossil fuel reserves together suggest the urgent need to secure long-lasting alternative and renewable energy resources. Here, we present a THz antenna integrated with a rectifier (rectenna) for harvesting infrared energy. We demonstrate a resonant bowtie antenna that has been optimized to produce highly enhanced localized fields at the bow tip. To benefit from this enhancement, the rectifier is realized between the overlapped antenna's arms using a 0.7 nm copper oxide. The thin film diode offers low zero bias resistance of 500 Ω, thus improving the impedance matching with the antenna. In addition, the rectenna prototype demonstrates high zero bias responsivity (4 A/W), which is critical in producing DC current directly from THz signals without the application of an external electric source, particularly for energy harvesting applications.

Design of highly efficient metallo-dielectric patch antennas for single-photon emission

Quantum emitters such as NV-centers or quantum dots can be used as single-photon sources. To improve their performance, they can be coupled to microcavities or nano-antennas. Plasmonic antennas offer an appealing solution as they can be used with broadband emitters. When properly designed, these antennas funnel light into useful modes, increasing the emission rate and the collection of single-photons. Yet, their inherent metallic losses are responsible for very low radiative efficiencies. Here, we introduce a new design of directional, metallo-dielectric, optical antennas with a Purcell factor of 150, a total efficiency of 74% and a collection efficiency of emitted photons of 99%.

Direct coupling of photonic modes and surface plasmon polaritons observed in 2-photon PEEM

We report the direct microscopic observation of optical energy transfer from guided photonic modes in an indium tin oxide (ITO) thin film to surface plasmon polaritons (SPP) at the surfaces of a single crystalline gold platelet. The photonic and SPP modes appear as an interference pattern in the photoelectron emission yield across the surface of the specimen. We explore the momentum match between the photonic and SPP modes in terms of simple waveguide theory and the three-layer slab model for bound SPP modes of thin metal films. We show that because the gold is thin (30-40 nm), two SPP modes exist and that momentum of the spatially confined asymmetric field mode coincides with the dominant mode of the ITO waveguide. The results demonstrate that photoemission electron microscopy (PEEM) can be an important tool for the observation of photonic to SPP interactions in the study of integrated photonic circuits.

Artificial intelligence in nanotechnology

During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

Surface plasmon delocalization in silver nanoparticle aggregates revealed by subdiffraction supercontinuum hot spots

The plasmonic resonances of nanostructured silver films produce exceptional surface enhancement, enabling reproducible single-molecule Raman scattering measurements. Supporting a broad range of plasmonic resonances, these disordered systems are difficult to investigate with conventional far-field spectroscopy. Here, we use nonlinear excitation spectroscopy and polarization anisotropy of single optical hot spots of supercontinuum generation to track the transformation of these plasmon modes as the mesoscopic structure is tuned from a film of discrete nanoparticles to a semicontinuous layer of aggregated particles. We demonstrate how hot spot formation from diffractively-coupled nanoparticles with broad spectral resonances transitions to that from spatially delocalized surface plasmon excitations, exhibiting multiple excitation resonances as narrow as 13 meV. Photon-localization microscopy reveals that the delocalized plasmons are capable of focusing multiple narrow radiation bands over a broadband range to the same spatial region within 6 nm, underscoring the existence of novel plasmonic nanoresonators embedded in highly disordered systems.

Mass-producible and efficient optical antennas with CMOS-fabricated nanometer-scale gap

Optical antennas have been widely used for sensitive photodetection, efficient light emission, high resolution imaging, and biochemical sensing because of their ability to capture and focus light energy beyond the diffraction limit. However, widespread application of optical antennas has been limited due to lack of appropriate methods for uniform and large area fabrication of antennas as well as difficulty in achieving an efficient design with small mode volume (gap spacing < 10nm). Here, we present a novel optical antenna design, arch-dipole antenna, with optimal radiation efficiency and small mode volume, 5 nm gap spacing, fabricated by CMOS-compatible deep-UV spacer lithography. We demonstrate strong surface-enhanced Raman spectroscopy (SERS) signal with an enhancement factor exceeding 10⁸ from the arch-dipole antenna array, which is two orders of magnitude stronger than that from the standard dipole antenna array fabricated by e-beam lithography. Since the antenna gap spacing, the critical dimension of the antenna, can be defined by deep-UV lithography, efficient optical antenna arrays with nanometer-scale gap can be mass-produced using current CMOS technology.

Midfield wireless powering of subwavelength autonomous devices

We obtain an analytical bound on the efficiency of wireless power transfer to a weakly coupled device. The optimal source is solved for a multilayer geometry in terms of a representation based on the field equivalence principle. The theory reveals that optimal power transfer exploits the properties of the midfield to achieve efficiencies far greater than conventional coil-based designs. As a physical realization of the source, we present a slot array structure whose performance closely approaches the theoretical bound.

Engineering metal adhesion layers that do not deteriorate plasmon resonances

Adhesion layers, required to stabilize metallic nanostructures, dramatically deteriorate the performances of plasmonic sensors, by severely damping the plasmon modes. In this article, we show that these detrimental effects critically depend on the overlap of the electromagnetic near-field of the resonant plasmon mode with the adhesion layer and can be minimized by careful engineering of the latter. We study the dependence of the geometrical parameters such as layer thickness and shape on the near-field of localized plasmon resonances for traditional adhesion layers such as Cr, Ti, and TiO2. Our experiments and simulations reveal a strong dependence of the damping on the layer thickness, in agreement with the exponential decay of the plasmon near-field. We developed a method to minimize the damping by selective deposition of thin adhesion layers (<1 nm) in a manner that prevents the layer to overlap with the hotspots of the plasmonic structure. Such a designed structure enables the use of standard Cr and Ti adhesion materials to fabricate robust plasmonic sensors without deteriorating their sensitivity.

A combination of concave/convex surfaces for field-enhancement optimization: the indented nanocone

We introduce a design strategy to maximize the Near Field (NF) enhancement near plasmonic antennas. We start by identifying and studying the basic electromagnetic effects that contribute to the electric near field enhancement. Next, we show how the concatenation of a convex and a concave surface allows merging all the effects on a single, continuous nanoantenna. As an example of this NF maximization strategy, we engineer a nanostructure, the indented nanocone. This structure, combines all the studied NF maximization effects with a synergistic boost provided by a Fano-like interference effect activated by the presence of the concave surface. As a result, the antenna exhibits a NF amplitude enhancement of ~ 800, which transforms into ~1600 when coupled to a perfect metallic surface. This strong enhancement makes the proposed structure a robust candidate to be used in field enhancement based technologies. Further elaborations of the concept may produce even larger and more effective enhancements.