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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

The dielectric permittivity of a material encapsulates the essential physics of light-matter interaction into the material’s local response to optical excitation. Photo-induced modulation of the permittivity can enable an unprecedented level of control over the phase, amplitude, and polarization of light. Therefore, the detailed dynamic characterization of technology-relevant materials with substantially tunable optical properties and fast response times is a crucial step to realize tunable optical devices. This work reports on the extraordinarily large permittivity changes in zinc oxide thin films (up to −3.6 relative change in the real part of the dielectric permittivity at 1600 nm wavelength) induced by optically generated free carriers. We demonstrate broadband reflectance modulation up to 70% in metal-backed oxide mirrors at the telecommunication wavelengths, with picosecond-scale relaxation times. The epsilon near zero points of the films can be dynamically shifted from 8.5 µm to 1.6 µm by controlling the pump fluence. The modulation can be selectively enhanced at specific wavelengths employing metal-backed zinc oxide disks while maintaining picosecond-scale switching times. This work provides insights into the free-carrier assisted permittivity modulation in zinc oxide and could enable the realization of novel dynamic devices for beam-steering, polarizers, and spatial light modulators.

Plasmonic color printing with semicontinuous metal films is a lithography-free and environment-friendly method for generating nonfading bright colors. Such films are comprised of islands, metal nan...

Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. Here, we review recent progress in deep-learning-based photonic design by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks. We also comment on the challenges and perspectives of this emerging research direction. The application of deep learning to the design of photonic structures and devices is reviewed, including algorithm fundamentals.

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 metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom 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 metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE 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 metadevices with complex topologies and material compositions.

We present our computationally efficient approach to modeling tunable graphene-based active metadevices, where the integral multivariate surface conductivity is reformulated in the time and frequency domains with physically interpretable and fast-to-compute integration-free terms. The derivation is built on an expansion to power series of $ {z}= -\text {exp}(-\mu /k_{B}T)$ that converges very fast for non-zero chemical potential values. The model reveals interesting decomposition of graphene response into a universal constant term plus a damped oscillator (digamma functions in the frequency domain) plus non-oscillating correction terms for near-zero potentials. The number of terms in that series is analyzed theoretically for a given accuracy. In practice, only a few series terms are required, making our approach very efficient for simulation of active metasurfaces compared with direct integration of Kubo’s formulas. A simple performance test comparing run times with our code versus the numerical integration of the original Kubo’s formulas demonstrates a speedup exceeding 103. The proposed models can be critical for the initial ellipsometric characterization of graphene and advanced global optimization of graphene-controlled metadevices.

As plasmonic materials transition from three-dimensional to two-dimensional (2D) form, unique optical and electronic phenomena arise that are unattainable in bulk materials and conventional thin films. Exceptional sensitivity to external perturbations, including electrical biasing and optical excitation as well as quantum effects, are expected to emerge, as has been demonstrated in graphene. Similarly, metallic or plasmonic films with thicknesses down to a few monolayers, called transdimensional materials (TDMs), are predicted to exhibit remarkably strong tunability of their optical response. The unique properties of 2D materials and TDMs have established them as promising platforms for dynamic nanophotonic devices. While novel 2D materials have been widely explored for nanophotonic devices, until recently, there have been minimal studies on the evolution of the optical properties of plasmonic TDMs. In this article, we highlight progress in exploring the thickness-dependent optical response of plasmonic materials as they approach the 2D regime. Experimental and theoretical investigations of the plasmonic properties of 2D materials, such as graphene and tungsten diselenide, TDMs, and plasmonic thin films, are discussed, with a focus on prospects for their utilization in dynamically tunable flat optical devices or metasurfaces.

The Starshot lightsail project aims to build an ultralight spacecraft ("nanocraft") that can reach Proxima Centauri b in approximately 20 years, requiring propulsion with a relativistic velocity of ~60 000 km/s. The spacecraft's acceleration approach currently under investigation is based on applying the radiation pressure from a high-power laser array located on Earth to the spacecraft lightsail. However, the practical realization of such a spacecraft imposes extreme requirements to the lightsail's optical, mechanical, thermal properties. Within this work, we apply adjoint topology optimization and variational autoencoder-assisted inverse design algorithm to develop and optimize a silicon-based lightsail design. We demonstrate that the developed framework can provide optimized optical and opto-kinematic properties of the lightsail. Furthermore, the framework opens up the pathways to realizing a multi-objective optimization of the entire lightsail propulsion system, leveraging the previously demonstrated concept of physics-driven compressed space engineering

Discovering novel, unconventional optical designs in combination with advanced machine-learning assisted data analysis techniques can uniquely enable new phenomena and breakthrough advances in many areas including imaging, sensing, energy, and quantum information technology. It demonstrated that compared to other inverse-design approaches that require extreme computation power to undertake a comprehensive search within a large parameter space, machine learning assisted topology optimization can expand the design space while improving the computational efficiency. This talk will highlight our most recent findings on 1) merging topology optimization with artificial-intelligence-assisted algorithms and 2) integrating machine-learning based analysis with photonic design and quantum optical measurements.

In this talk we will highlight our most recent developments in 1) deep learning assisted optimization of photonic meta-structures and 2) machine learning-based algorithms for quantum photonic applications. We will discuss our studies on implementing deep-learning assisted topology optimization for advanced metasurface design development. We will also outline our recent work on merging topology optimization techniques with quantum device design development for achieving efficient on-chip integration. Finally, we will discuss approaches for implementing a novel convolutional neural network-based technique for real-time material defect metrology and quantum super-resolution microscopy applications.

Control over the permittivity and relaxation time in materials is important to gain control over the amplitude, phase and polarization of light. In this presentation, we report our work on controlling the static and dynamic optical properties of transition metal nitrides and transparent conducting oxides for a wide array of nanophotonic applications and nonlinear optical demonstrations. We find the carrier relaxation mechanism in titanium and zirconium nitride by pump-probe studies near the ENZ. We demonstrate high-harmonic generation in refractory TiN films. We show large changes in the epsilon-near-zero points in cadmium oxide via yttrium doping, for optically controlled, mid-IR reflectance modulation. In undoped zinc oxide, we demonstrate unity-order permittivity modulation by optical doping, with 20ps response time. With TiN-AZO Berreman-metasurfaces, we demonstrate simultaneous picosecond switching at telecom wavelengths, and nanosecond switching in the visible regime. Employing the ENZ effects in aluminum-doped zinc oxide, we demonstrate nonlinear optical phenomena spanning optical time-reversal to negative refraction. Our work will aid the development of dynamic devices for a wide array of dynamic optical applications.

The realization of integrated quantum photonic circuits is crucial for scalable quantum computing and information processing applications. One of the milestones is the realization of highly-efficient coupling of pre-determined single-photon sources into the on-chip environment. Along with solid-state quantum sources, like quantum dots and defects in solids, quantum defects in 2D materials have attracted significant interest due to their quantum emission properties and stability. We have developed an adjoint-topology optimization framework to improve the coupling efficiency of color centers in hBN to the silicon nitride platform. We have demonstrated more than 75% coupling efficiency with a topology optimized coupler.

Precise control over photonic states can be achieved in pre-engineered materials, like photonic crystals and meta-structures. However, high fabrication precisions are required due to the sensitivit...

The field of thermoplasmonics has thrived in the past decades because it uniquely provides remotely controllable nanometer-scale heat sources that have augmented numerous technologies. Despite the extensive studies on steady-state plasmonic heating, the dynamic behavior of the plasmonic heaters in the nanosecond regime has remained largely unexplored, yet such a time scale is indeed essential for a broad range of applications such as photocatalysis, optical modulators, and detectors. Here, we use two distinct techniques based on the temperature-dependent surface reflectivity of materials, optical thermoreflectance imaging (OTI) and time-domain thermoreflectance (TDTR), to comprehensively investigate plasmonic heating in both spatial and temporal domains. Specifically, OTI enables the rapid visualization of plasmonic heating with sub-micron resolution, outperforming a standard thermal camera, and allows us to establish the connection between the optical absorptance and heating efficiency as well as to analyze plasmonic heating dynamics on the millisecond scale. Using the TDTR technique, we, for the first time, study the optical resonance-dependent heat-transfer dynamics of a nanometer-scale plasmonic structure in the nanosecond regime and use a detailed computational model to extract the impulse response and thermal interface conductance of a multilayer plasmonic structure. The study reveals a quantitative relationship between the dimensions of the nanopatterned structure and its spatiotemporal thermal response to the light pulse excitation, a thermoplasmonic effect resulting from the spatial distribution of the absorbed electromagnetic energy. We also conclude that the two thermoreflectance techniques provide necessary feedback to nanoscale thermoplasmonic heat management, for which optimization in either heating power or temperature decay speed is needed.

On-demand, switchable phase transitions between topologically nontrivial and trivial photonic states are demonstrated. Specifically, it is shown that the integration of a two-dimensional array of coupled ring resonators within a thermal heater array enables unparalleled control over topological protection of photonic modes. Importantly, auxiliary control over a 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.

Active control over the flow of light is highly desirable because of its applicability to information processing, telecommunication, and spectroscopic imaging. In this paper, by employing the tunability of carrier density in a 1 nm titanium nitride (TiN) film, we numerically demonstrate deep phase modulation (PM) in an electrically tunable gold strip/TiN film hybrid metasurface. A 337° PM is achieved at 1.550 μm with a 3% carrier density change in the TiN film. We also demonstrate that a continuous 180° PM can be realized at 1.537 μm by applying a realistic experiment-based gate voltage bias and continuously changing the carrier density in the TiN film. The proposed design of active metasurfaces capable of deep PM near the wavelength of 1.550 μm has considerable potential in active beam steering, dynamic hologram generation, and flat photonic devices with reconfigurable functionalities.

Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements.

Dynamic control over the permittivity of materials enables control over the amplitude, phase, and polarization of light. Thus, to realize practical tunable devices, it is important to perform a detailed dynamic characterization of technology-relevant materials with substantially tunable optical properties. In this work, we demonstrate extraordinarily large, unity-order permittivity modulation in zinc oxide through interband pumping. The large permittivity changes actively enable large reflectance modulation in both lithography-free mirrors (70% at 31.6 mJ/cm2 pump) and nanodisk resonators (55% at 7.6 mJ/cm2 pump fluence). The relaxation time for this response is 20 ps. We explore the physical origins of the permittivity modulation and determine the physical limits. The results of this study will advance the realization of ultrafast dynamic optical devices for optical switching, beam-steering, and spectroscopy.

Controlling the permittivity of materials enables control over the amplitude, phase and polarization of light interacting with them. Tailorable and tunable transparent conducting oxides have applications in optical switching, beam steering, imaging, sensing, and spectroscopy. In this work, we experimentally demonstrate wide tailoring and tuning of the optical properties of oxides to achieve fast switching with large modulation depths. In cadmium oxide, the permittivity and the epsilon-near-zero points can be tailored via yttrium doping to achieve large, ENZ-enhanced mid-IR reflectance modulation. In zinc oxide, the permittivity is tuned by interband pumping, achieving large reflectance modulation in the telecom regime. With aluminum-doped zinc oxide, we demonstrate tailorable Berreman-type absorbers that can achieve ultrafast switching in the telecom frequencies. Our work will pave the way to practical optical switching spanning the telecom to the mid-infrared wavelength regimes.

The developed design framework employs the D-Wave to enable global optimization of meta-devices with complex topologies and material composition. The framework opens up the pathways to solving broad range of highly-constrained optimization problems of nanophotonics.

We report the discovery of room-temperature quantum emitters in silicon nitride. Photophysical analysis reveals bright (>105 counts/s), stable, and pure single-photon emission with an average of g(2)(0) of about 0.25 collected from a hundred emitters.

The recent development of fabrication techniques and material platforms allows addressing various multi-constrained problems in many areas including on-chip circuitry, imaging, sensing, energy, and quantum information technology. However, these require advanced design development methods, which can go beyond conventional optimization. We will discuss recent and promising advances in the development of photonic components based on topology optimization. One of the main aspects of current work is expanding and streamlining a conventional meta-device design methodology to a global optimization space. Specifically, we will cover our recent efforts on coupling topology optimization techniques with deep generative algorithms for dielectric/plasmonic metastructure design development.

Bound states in the continuum (BIC) attracted great attention in the photonics community. The existence of such states has led to numerous applications, including optical sensors and filters. Here we report on the approach to externally tune a magnitude and spectral position of high-Q resonances, associated with not symmetry-protected BIC state in silicon nitride (Si3N4) photonic crystals. We show that BIC properties can be controlled by the external thermal impact. These results can be used to construct compact and thermally stable optical sensors immune to harsh environmental conditions.

Within this work, we will discuss our recent and promising advances in the development of machine learning assisted optimization schemes for plasmonic/photonic meta-structures design development. We will showcase that by coupling adversarial autoencoding network with topology optimization, it is possible to expand conventional meta-device design methodology to a global optimization space. In the second part of the talk, we will cover our recent effort on coupling machine learning classification/regression algorithms with quantum measurements. Particularly, we will show that the synergy between advanced machine learning assisted data analysis with quantum optical measurements dramatically reduces data collection time as well as increases the accuracy of the measurements.

Hot carriers in plasmonic nanostructures, generated via plasmon decay, play key roles in applications such as photocatalysis and in photodetectors that circumvent bandgap limitations. However, direct experimental quantification of steady-state energy distributions of hot carriers in nanostructures has so far been lacking. We present transport measurements from single-molecule junctions, created by trapping suitably chosen single molecules between an ultrathin gold film supporting surface plasmon polaritons and a scanning probe tip, that can provide quantification of plasmonic hot-carrier distributions. Our results show that Landau damping is the dominant physical mechanism of hot-carrier generation in nanoscale systems with strong confinement. The technique developed in this work will enable quantification of plasmonic hot-carrier distributions in nanophotonic and plasmonic devices.

We have coupled adversarial autoencoder deep generative network with adjoined topology optimization technique for metasurface design optimization. We have demonstrated that such approach ensures not only efficient optimization search of highly efficient metasurface designs but also provides unparalleled control over compressed design space distribution. The latter fact assures scalability of the approach to highly-constrained optimization problems.

Identification of a suitable source of single photons via second-order autocorrelation function measurement within an array of thousand possible candidates is a routine, key step of any practical realization in quantum optics. Within this work, we have shown that machine learning algorithms enable high precision classification between “single” and “not single” quantum emitters based on sparse autocorrelation data measurement and require 1 min. Machine learning assisted classification, done on a sparse 1-s dataset, provides ~85% accuracy of “single”/“not single” emitter identification versus only 57% of the conventional Levenberg-Marquardt approach.

Time-varying metasurfaces are emerging as a powerful instrument for the dynamical control of the electromagnetic properties of a propagating wave. Here we demonstrate an efficient time-varying metasurface based on plasmonic nano-antennas strongly coupled to an epsilon-near-zero (ENZ) deeply subwavelength film. The plasmonic resonance of the metal resonators strongly interacts with the optical ENZ modes, providing a Rabi level spitting of ∼30%. Optical pumping at frequency ω induces a nonlinear polarization oscillating at 2ω responsible for an efficient generation of a phase conjugate and a negative refracted beam with a conversion efficiency that is more than 4 orders of magnitude greater compared to the bare ENZ film. The introduction of a strongly coupled plasmonic system therefore provides a simple and effective route towards the implementation of ENZ physics at the nanoscale.

We experimentally demonstrate broadband, polarization-sensitive reflection modulation and picosecond relaxation-times with TiN-ZnO metal-dielectric resonators. We perform a rigorous investigation of the effects of pump-powers, probe-wavelengths, and polarizations on the device performance.

A non-fading and environment-friendly technique of generation of bright vivid colors is achieved through femtosecond laser modification of semicontinuous Ag films, which thermally induces changes in the nanostructures resulting in the variation of optical spectra.

We demonstrate significant epsilon-near-zero point shifts (11.3 pm to 5.3 pm), extraordinarily large (135%) optically-induced reflection modulation with picosecond response times, and carrier relaxation time-engineering in cadmium oxide via Yttrium doping.

Single quantum emitters offer useful functionalities for quantum optics, but measurements of their properties are time-consuming. We demonstrate that machine learning dramatically reduces data collection time (1s), increasing the accuracy of second-order autocorrelation measurements (>90%).

The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(90%).

This work reports on the extraordinarily large optically-induced permittivity changes in zinc oxide thin films. Broadband, large reflectance modulation is demonstrated in both planar films and patterned resonators in the NIR wavelength range.

Light–matter interactions at a particular point in a material may be dominated by properties of the medium at this point, or they could be affected by the electromagnetic properties of the medium in the surrounding regions. In the former case, the medium is said to be local, while in the latter, it is nonlocal. Recent studies of light–matter interactions in composite optical metamaterials showed that nonlocal effects enable new optical phenomena that are not acounted for by the conventional, local effective medium description. Up until now the majority of studies focused on metamaterials with spatially uniform material parameters. However, optical metamaterials with electromagnetic material parameters gradually changing from positive to negative values, so-called transition materials, have been predicted to induce a strong enhancement of the local electric or magnetic field in the vicinity of the zero refractive index point. This opens new opportunities for sensing and low-intensity nonlinear optical appl...

Due to their exceptional plasmonic properties, noble metals such as gold and silver have been the materials of choice for the demonstration of various plasmonic and nanophotonic phenomena. However, noble metals' softness, lack of tailorability and low melting point along with challenges in thin film fabrication and device integration have prevented the realization of real-life plasmonic devices.In the recent years, titanium nitride (TiN) has emerged as a promising plasmonic material with good metallic and refractory (high temperature stable) properties. The refractory nature of TiN could enable practical plasmonic devices operating at elevated temperatures for energy conversion and harsh-environment industries such as gas and oil. Here we report on the temperature dependent dielectric functions of TiN thin films of varying thicknesses in the technologically relevant visible and near-infrared wavelength range from 330 nm to 2000 nm for temperatures up to 900 0C using in-situ high temperature ellipsometry. Our findings show that the complex dielectric function of TiN at elevated temperatures deviates from the optical parameters at room temperature, indicating degradation in plasmonic properties both in the real and imaginary parts of the dielectric constant. However, quite strikingly, the relative changes of the optical properties of TiN are significantly smaller compared to its noble metal counterparts. Using simulations, we demonstrate that incorporating the temperature-induced deviations into the numerical models leads to significant differences in the optical responses of high temperature nanophotonic systems. These studies hold the key for accurate modeling of high temperature TiN based optical elements and nanophotonic systems for energy conversion, harsh-environment sensors and heat-assisted applications., Comment: 23 pages, 9 figures and 5 tables

We outline future directions in the development of a platform for high-speed integrated quantum photonics, the use of plasmonics to outpace quantum decoherence and the application of machine-learning techniques for photonics designs and quantum optical measurements.

Transparent Conducting Oxide (TCO) materials are degenerately-doped, wide-bandgap semiconductors which exhibit simultaneous high-conductivity and visible transparency. These unique properties are well known and frequently exploited for technologies such as touch-screen devices. In recent years, TCOs have been recognized as a promising material platform for nanophotonic devices, namely because of their simple, compatible fabrication, low-losses, dynamic modulation, and novel low-index properties. In this talk, I will highlight recent progress in the field of TCO-based nanophotonics, share our ongoing results and observations, and discuss future research challenges and directions. In particular, I will discuss our progress in developing metal-dielectric hybrid metasurfaces which incorporate TCOs for all-optical, ultrafast switching. Here, we incorporate defect-rich zinc oxide with a refractory titanium nitride metasurface for efficient light modulation at near-terahertz switching frequencies. My talk will also focus on TCO films for studying and observing low-index phenomena. Our recent work with aluminum-doped zinc oxide films demonstrates the ability for low-index materials to both enhance negative refraction and engender strongly coupled plasmonic systems with large room-temperature Rabi frequencies. Our work signifies the strong potential for incorporating transparent conducting oxides into plasmonic and nanophotonic devices to provide advances toward practical technologies and depth in scientific understanding.

Advanced nanophotonic concepts, such as photonic crystals, metamaterials and metasurfaces, have enabled unique functionalities including, negative refraction, hyperbolic dispersion, optical magnetism, control of quantum emitters, epsilon-near-zero phenomena, and enhanced light-matter interactions. With the recent development of new plasmonic/photonic materials and nano-fabrication techniques, nanophotonic devices are now capable of providing novel solutions to global challenges including world energy consumption, rapid and accurate chemical/biological detection, quantum computing/security, telecom information densities, and space exploration. These problems are inherently complex due to their multi-disciplinary nature, requiring a manifold of stringent constraints in conjunction with optical performance. Topology optimization has emerged as a successful architect for the systematic design of photonic structures and provide solutions for aforementioned the problems. In this talk, we will highlight recent progress in the field of topology optimization for nanophotonics, share our ongoing results and observations, and discuss future research challenges and directions. In particular, we will discuss our progress in developing solar- thermophotovoltaics components using topology optimization. One of the main aspects of our current work is expanding and streamlining conventional meta-device design methodology to a global optimization space by 1) advancing topology optimization via artificial-intelligence-assisted algorithms and 2) by extending the optimization parameter space into materials domain through optical materials development and multiphysics simulations.

Light control in photonic systems and an improved robustness to imperfections or disorder is in high demand. Recently it has been shown that by breaking time reversal symmetry in photonic systems, it is possible to realize topologically protected states which are resistant to perturbations and back-scattering. This effort has resulted in an increased interest in a new class of topologically ordered optical systems - photonic topological insulators. Some of the approaches to realize topologically protected photonic states include using metamaterials exhibiting a magneto-optical/nonlinear responses, engineering photonic crystal dispersion, as well as introducing synthetic magnetic field for photons. Precise control of topologically protected states can potentially open new frontiers of light matter interaction and lead to a number of applications, such as topologically protected memory/logic devices, compact optical isolators, unidirectional waveguide systems and numerous quantum communication applications. Recent investigations reveal transparent conduction oxides (TCOs) (such as Indium-tin-oxide (ITO) and Al-doped ZnO (AZO)) as a promising building block for on-chip photonics and planar optics applications with ultrahigh modulation capabilities (< 1 [ps]). Within this work we have demonstrated that by integrating a TCO material platform with a standard CMOS-compatible SOI technology, it is possible to get unparalleled ultrafast optical/electrical control of synthetic gauge magnetic field. We have considered a silicon resonator array (510-nm-wide and 220-nm-height) on silicon dioxide integrated with AZO as a dynamically tunable element. It was demonstrated that transitions between topologically protected and non-protected states can be achieved by electrical/optical tuning of 50-nm AZO film.

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., Comment: Adv. Quantum Technol. 2020