arXiv subjects

Alexander V. Kildishev

Publications and source records attributed to Alexander V. Kildishev.

At least 37 records · Page 2Linked to original sources

arXiv2021

High-order accurate schemes for Maxwell's equations with nonlinear active media and material interfaces

We describe a fourth-order accurate finite-difference time-domain scheme for solving dispersive Maxwell's equations with nonlinear multi-level carrier kinetics models. The scheme is based on an efficient single-step three time-level modified equation approach for Maxwell's equations in second-order form for the electric field coupled to ODEs for the polarization vectors and population densities of the atomic levels. The resulting scheme has a large CFL-one time-step. Curved interfaces between different materials are accurately treated with curvilinear grids and compatibility conditions. A novel hierarchical modified equation approach leads to an explicit scheme that does not require any nonlinear iterations. The hierarchical approach at interfaces leads to local updates at the interface with no coupling in the tangential directions. Complex geometry is treated with overset grids. Numerical stability is maintained using high-order upwind dissipation designed for Maxwell's equations in second-order form. The scheme is carefully verified for a number of two and three-dimensional problems. The resulting numerical model with generalized dispersion and arbitrary nonlinear multi-level system can be used for many plasmonic applications such as for ab initio time domain modeling of nonlinear engineered materials for nanolasing applications, where nano-patterned plasmonic dispersive arrays are used to enhance otherwise weak nonlinearity in the active media.

math.NA↗

arXiv2021

Efficient Topology Optimized Couplers for On-Chip Single-photon Sources

Room temperature single-photon sources (SPSs) are critical for the emerging practical quantum applications such as on-chip photonic circuity for quantum communications systems and integrated quantum sensors. However, direct integration of an SPS into on-chip photonic systems remains challenging due to low coupling efficiencies between the SPS and the photonic circuitry that often involve size mismatch and dissimilar materials. Here, we develop an adjoint topology optimization scheme to design high-efficiency couplers between a photonic waveguide and SPS in hexagonal boron nitride (hBN). The algorithm accounts for fabrication constraints and the SPS location uncertainty. First, a library of designs for the different positions of the hBN flake containing an SPS with respect to a Si$_{3}$N$_{4}$ waveguide is generated, demonstrating an average coupling efficiency of 78%. Then, the designs are inspected with dimensionality reduction technique to investigate the relationship between the device geometry (topology) and performance. The fundamental, physics-based intuition gained from this approach could enable the design of high-performance quantum devices

physics.optics↗

arXiv2021

Machine learning assisted quantum super-resolution microscopy

One of the main characteristics of optical imaging systems is the spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $\sqrt{n}$ improvement in the spatial resolution by measuring the n-th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

physics.optics↗

arXiv2021

Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems

In recent years, there is a growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework's performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.

quant-ph↗

arXiv2020

Lithography-free plasmonic color printing with femtosecond laser on semicontinuous silver films

Plasmonic color printing with semicontinuous metal films is a lithography-free, non-fading, and environment-friendly method of generation of bright colors. Such films are comprised of metal nanoparticles, which resonate at different wavelengths upon light illumination depending on the size and shape of the nanoparticles. To achieve an experimentally demonstrated structure that was optimized in terms of broader color range and increased stability, variable Ag semicontinuous metal films were deposited on a metallic mirror with a sub-wavelength-thick dielectric spacer. Femtosecond laser post-processing was then introduced to extend the color gamut through spectrally induced changes from blue to green, red, and yellow. Long-term stability and durability of the structures were addressed to enable non-fading colors with an optimized overcoating dielectric layer. The thickness of the proposed designs is on the order of 100 nanometers, and it can be deposited on any substrate. These structures generate a broad range of long-lasting colors in reflection that can be applied to real-life artistic or technological applications with a spatial resolution on the order of 0.3 mm or less.

physics.app-ph↗

arXiv2020

Enabling optical steganography, data storage, and encryption with plasmonic colors

Plasmonic color generation utilizing ultra-thin metasurfaces as well as metallic nanoparticles hold a great promise for a wide range of applications, including color displays, data storage, and information encryption due to its high spatial resolution and mechanical/chemical stability. Most of the recently demonstrated systems generate static colors; however, more advanced applications such as data storage require fast and flexible means to tune the plasmonic colors, while keeping them vibrant and stable. Here, a surface-relief aluminum metasurface that reflects polarization-tunable plasmonic colors is designed and experimentally demonstrated. Excitation of localized surface plasmons encodes discrete combinations of the incident and reflected polarized light into diverse colors. A single storage unit - a nanopixel - stores a multiple-bit piece of information in the orientation of its constituent nanoantennae. This information is then reliably retrieved by inspecting the reflected color sequence with two linear polarizers. It is the broad color variability and high spatial resolution of the proposed encoding approach that supports a strong promise for rapid parallel read-out and encryption of high-density optical data. Our method also enables the robust generation of dynamic kaleidoscopic images with no detrimental "cross-talk" effect. The approach opens up a new route for advanced dynamic steganography, high-density parallel-access optical data storage, and optical information encryption.

physics.optics↗

arXiv2020

Machine learning assisted global optimization of photonic devices

Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable, "by design" effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or meta-atoms. Such approach can not provide the global solution to a complex optimization problem where both meta-atoms shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine-learning-assisted global optimization framework for photonic meta-devices design. We demonstrate that using an adversarial autoencoder coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the meta-devices configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of adversarial autoencoder based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design "intuition" by revealing the underlying physics of the optical performance of meta-devices with complex topologies and material compositions.

physics.optics↗

arXiv2020

Single and multi-mode directional lasing from arrays of dielectric nanoresonators

The strong electric and magnetic resonances in dielectric subwavelength structures have enabled unique opportunities for efficient manipulation of light-matter interactions. Besides, the dramatic enhancement of nonlinear light-matter interactions near so-called bound states in the continuum (BICs) has recently attracted enormous attention due to potential advancements in all-optical and quantum computing. However, the experimental realizations and the applications of high- Q factor resonances in dielectric resonances in the visible regime have thus far been considerably limited. In this work, we explore the interplay of electric and magnetic dipoles in arrays of dielectric nanoresonators to enhance light-matter interaction. We report on the experimental realization of high-Q factor resonances in the visible through the collective diffractive coupling of electric and magnetic dipoles. Providing direct physical insights, we also show that coupling the Rayleigh anomaly of the array with the electric and magnetic dipoles of the individual nanoresonators can result in the formation of different types of BICs. We utilize the resonances in the visible regime to achieve lasing action at room temperature with high spatial directionality and low threshold. Finally, we experimentally demonstrate multi-mode, directional lasing and study the BIC-assisted lasing mode engineering in arrays of dielectric nanoresonators. We believe that our results enable a new range of applications in flat photonics through realizing on-chip controllable single and multi-wavelength micro-lasers.

physics.optics↗

arXiv2020

Machine-Learning-Assisted Metasurface Design for High-Efficiency Thermal Emitter Optimization

With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion, information technologies, chemical/biological sensing, space exploration, quantum computing, and secure communication. Addressing grand challenges poses inherently complex, multi-disciplinary problems with a manifold of stringent constraints in conjunction with the required system's performance. Conventional optimization techniques have long been utilized as powerful tools to address multi-constrained design tasks. One example is so-called topology optimization that has emerged as a highly successful architect for the advanced design of non-intuitive photonic structures. Despite many advantages, this technique requires substantial computational resources and thus has very limited applicability to highly constrained optimization problems within high-dimensions parametric space. In our approach, we merge the topology optimization method with machine learning algorithms such as adversarial autoencoders and show substantial improvement of the optimization process by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.

physics.optics↗

arXiv2019

Rapid classification of quantum sources enabled by machine learning

Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. We have implemented supervised machine learning-based classification of quantum emitters as "single" or "not-single" based on their sparse autocorrelation data. Our method yields a classification accuracy of over 90% within an integration time of less than a second, realizing roughly a hundredfold speedup compared to the conventional, Levenberg-Marquardt approach. We anticipate that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices and can be directly extended to other quantum optical measurements.

physics.optics↗

arXiv2019

Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Machine-learning tasks performed by neural networks demonstrated useful capabilities for producing reliable, and repeatable intelligent decisions. Integrated photonics, leveraging both component miniaturization and the wave-nature of the signals, can potentially outperform electronics architectures when performing inference tasks. However, the missing photon-photon force challenges non-volatile photonic device-functionality required for efficient neural networks. Here we present a novel concept and optimization of multi-level discrete-state non-volatile photonic memory based on an ultra-compact (<4um) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. An optimized electro-thermal switching mechanism, induced by Joule heating through tungsten contacts, is engineered. This operation allows to change the phase of the GSST film thus providing weight updating functionality to the network. We show that a 5 V pulse-train (<1 us, 20 pulses) applied to a serpentine contact produces crystallization and a single pulse of longer duration (2 us) amorphization, used to set the analog synaptic weights of a neuron. Emulating an opportunely trained 100x100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 93% inference accuracy and robustness towards noise when performing predictions of unseen data

physics.app-ph↗

arXiv2019

Chip-compatible quantum plasmonic launcher

Integrated on-demand single-photon sources are critical for the implementation of photonic quantum information processing systems. To enable practical quantum photonic devices, the emission rates of solid-state quantum emitters need to be substantially enhanced and the emitted signal must be directly coupled to an on-chip circuitry. The photon emission rate speed-up is best achieved via coupling to plasmonic antennas, while on-chip integration can be easily obtained by directly coupling emitters to photonic waveguides. The realization of practical devices requires that both the emission speed-up and efficient out-couping are achieved in a single architecture. Here, we propose a novel platform that effectively combines on-chip compatibility with high radiative emission rates, a quantum plasmonic launcher. The proposed launchers contain single nitrogen-vacancy (NV) centers in nanodiamonds as quantum emitters that offer record-high average fluorescence lifetime shortening factors of about 7000 times. Nanodiamonds with single NV are sandwiched between two silver films that couple more than half of the emission into in-plane propagating surface plasmon polaritons. This simple, compact, and scalable architecture represents a crucial step towards the practical realization of high-speed on-chip quantum networks.

quant-ph↗

arXiv2019

On-chip microwave-spin-plasmon interface (MSPI)

On-chip scalable integration represents a major challenge for practical quantum devices. One particular challenge is to implement on-chip optical readout of spins in diamond. This readout requires simultaneous application of optical and microwave fields along with an efficient collection of fluorescence. The readout is typically accomplished via bulk optics and macroscopic microwave transmission structures. We experimentally demonstrate an on-chip integrated structure for nitrogen vacancy (NV) spin-based applications, implemented in a single material layer with one patterning step. A nanodiamond with multiple NV centres is positioned at the end of the groove waveguide milled in a thick gold film. The gold film carries the microwave control signal while the groove waveguide acts as a fluorescence collector, partially filtering out the pump excitation. As a result, the device dimensions and fabrication complexity are substantially reduced. Our approach will foster further development of ultra-compact nanoscale quantum sensors and quantum information processing devices on a monolithic platform. NV centre-based nanoscale sensors are the most promising application of the developed interface.

physics.app-ph↗

arXiv2019

Photonic topological phase transition on demand

On-demand, switchable phase transitions between topologically non-trivial and trivial photonic states are demonstrated. Specifically, it is shown that integration of a 2D array of coupled ring resonators within a thermal heater array enables unparalleled control over topological protection of photonic modes. Importantly, auxiliary control over spatial phase modulation opens up a way to guide topologically protected edge modes along generated virtual boundaries. The proposed approach can lead to practical realizations of topological phase transitions in many photonic applications, including topologically protected photonic memory/logic devices, robust optical modulators, and switches.

physics.optics↗

arXiv2019

Deterministic integration of single nitrogen-vacancy centers into nanopatch antennas

Quantum emitters coupled to plasmonic nanoantennas produce single photons at unprecedentedly high rates in ambient conditions. This enhancement of quantum emitters' radiation rate is based on the existence of optical modes with highly sub-diffraction volumes supported by plasmonic gap nanoantennas. Nanoantennas with gap sizes on the order of few nanometers have been typically produced using various self-assembly or random assembly techniques. Yet, the difficulty of controllably fabricate nanoantennas with the smallest mode sizes coupled to pre-characterized single emitters until now has remained a serious issue plaguing the development of quantum plasmonic devices. We demonstrate the transfer of nanodiamonds with single nitrogen-vacancy (NV) centers to an epitaxial silver substrate and their subsequent deterministic coupling to plasmonic gap nanoantennas. Through fine control of the assembled nanoantenna geometry, a dramatic shortening of the NV fluorescence lifetime was achieved. We furthermore show that by preselecting NV centers exhibiting a photostable spin contrast, a coherent spin dynamics can be measured in the coupled configuration. The demonstrated approach opens unique applications of plasmon-enhanced quantum emitters for integrated quantum information and sensing devices.

quant-ph↗

arXiv2018

Formation of Bound States in the Continuum in Hybrid Plasmonic-Photonic Systems

A bound state in the continuum (BIC) is a localized state of an open structure with access to radiation channels, yet it remains highly confined with, in theory, infinite lifetime and quality factor. There have been many realizations of such exceptional states in dielectric systems without loss. However, realizing BICs in systems with losses such as plasmonics remains a challenge. In this Letter, we explore the possibility of realizing BICs in a hybrid plasmonic-photonic structure consisting of a plasmonic grating coupled to a dielectric optical waveguide. The plasmonic-photonic system supports two distinct groups of BICs: symmetry protected BICs at the Γ-point and off-Γ Friedrich-Wintgen BICs. The photonic waveguide modes are strongly coupled to the gap plasmons in the grating leading to an avoided crossing behavior with a high value of Rabi splitting of 150 meV. Moreover, we show that the strong coupling significantly alters the band diagram of the hybrid system revealing opportunities for supporting stopped light at an off-Γ wide angular span.

physics.optics↗

arXiv2018

All-optical Nonlinear Activation Function for Photonic Neural Networks

With the recent successes of neural networks (NN) to perform machine-learning tasks, photonic-based NN designs may enable high throughput and low power neuromorphic compute paradigms since they bypass the parasitic charging of capacitive wires. Thus, engineering data-information processors capable of executing NN algorithms with high efficiency is of major importance for applications ranging from pattern recognition to classification. Our hypothesis is therefore, that if the time-limiting electro-optic conversion of current photonic NN designs could be postponed until the very end of the network, then the execution time of the photonic algorithm is simple the delay of the time-of-flight of photons through the NN, which is on the order of picoseconds for integrated photonics. Exploring such all-optical NN, in this work we discuss two independent approaches of implementing the optical perceptrons nonlinear activation function based on nanophotonic structures exhibiting i) induced transparency and ii) reverse saturated absorption. Our results show that the all-optical nonlinearity provides about 3 and 7 dB extinction ratio for the two systems considered, respectively, and classification accuracies of an exemplary MNIST task of 97% and near 100% are found, which rivals that of software based trained NNs, yet with ignored noise in the network. Together with a developed concept for an all-optical perceptron, these findings point to the possibility of realizing pure photonic NNs with potentially unmatched throughput and even energy consumption for next generation information processing hardware.

physics.app-ph↗

arXiv2018

Full-wave analysis of reverse saturable absorption in time-domain

An advanced full-wave time-domain numerical model for reverse saturable absorption (RSA) is presented and verified against established methods. Rate equations, describing atomic relaxations and excitation dynamics, are coupled to Maxwell equations by using a Lorentzian oscillator. These oscillator models the kinetics-dependent light-matter interaction in the form of averaged polarizations. The coupled equations are discretized in space and time using a finite-difference time-domain method which provides a versatile platform to design complex structures and integrate diverse material models. We believe that our models are crucial tools enabling for the realization and optimization of optical limiting and all-optical switching systems.

physics.optics↗