Nanophotonic Components Research Articles

(Invited) Tackling Interface Challenges for Shallow Diamond Color Centers

Spin defects in wide-bandgap semiconductors, such as color centers in diamond, can be highly sensitive to local (nanoscale) changes in magnetic field, temperature, and strain. These solid-state quantum sensors have certain advantages over their atomic counterparts owing to their room-temperature operation without the need for vacuum components and the relative ease of photonic- and RF-component integration. However, near-surface quantum defects exhibit spin decoherence and most of the light emitted is trapped within the bulk crystal due total internal reflection at the interface. In this presentation, I will summarize our recent work towards better understanding and addressing these interface challenges, including the modeling and experimental characterization of radiative emission of near-surface emitters, design of nanophotonic components to improve light extraction, and surface analysis and chemical termination techniques aimed at improving spin coherence of emitters.

Control of work functions of nanophotonic components

Work function is an essential material’s property playing important roles in electronics, photovoltaics, and more recently, in nanophotonics. We have studied effects of organic, and inorganic dielectric materials on work functions of Au films in single layered, and multilayered structures. We found that measured work function of metallic surfaces can be affected by dielectric materials situated 10–100 nm away from the metallic surface. We have found that, (i) the glass underneath ~ 50 nm gold slab reduces the work function of gold, (ii) Rh590:PMMA increases the work function of a gold film deposited on top of the polymer, and (iii) reduces it if Rh590:PMMA is deposited on top of Au. (iv) With increase of the Rh590 concentration in PMMA, n, the work function first decreases (at n < 64 g/l), and then increases (at n > 64 g/l). (v) The work function of a Fabry–Perot cavity or an MIM waveguide is almost the same as that of single Au films of comparable thickness. The experimental results can be qualitatively explained in terms of a simple model taking into account adhesion of charged molecules to a metallic surface, and formation of a double layer of charges accelerating or decelerating electrons exiting the metal and decreasing or increasing the work function.

Direct programming of confined surface phonon polariton resonators with the plasmonic phase-change material In3SbTe2

Tailoring light-matter interaction is essential to realize nanophotonic components. It can be achieved with surface phonon polaritons (SPhPs), an excitation of photons coupled with phonons of polar crystals, which also occur in 2d materials such as hexagonal boron nitride or anisotropic crystals. Ultra-confined resonances are observed by restricting the SPhPs to cavities. Phase-change materials (PCMs) enable non-volatile programming of these cavities based on a change in the refractive index. Recently, the plasmonic PCM In3SbTe2 (IST) was introduced which can be reversibly switched from an amorphous dielectric state to a crystalline metallic one in the entire infrared to realize numerous nanoantenna geometries. However, reconfiguring SPhP resonators to modify the confined polaritons modes remains elusive. Here, we demonstrate direct programming of confined SPhP resonators by phase-switching IST on top of a polar silicon carbide crystal and investigate the strongly confined resonance modes with scanning near-field optical microscopy. Reconfiguring the size of the resonators themselves result in enhanced mode confinements up to a value of λ/35.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{\\boldsymbol{\\lambda }}}}}}{{{{{\\boldsymbol{/}}}}}}{{{{{\\boldsymbol{35}}}}}}{{{{{\\boldsymbol{.}}}}}}$$\\end{document} Finally, unconventional cavity shapes with complex field patterns are explored as well. This study is a first step towards rapid prototyping of reconfigurable SPhP resonators that can be easily transferred to hyperbolic and anisotropic 2d materials.

Computation cost reduction in 3D shape optimization of nanophotonic components

Inverse design methodologies effectively optimize many design parameters of a photonic device with respect to a primary objective, uncovering locally optimal designs in a typically non-convex parameter space. Often, a variety of secondary objectives (performance metrics) also need to be considered before fabrication takes place. Hence, a large collection of optimized designs is useful, as their performance on secondary objectives often varies. For certain classes of components such as shape-optimized devices, the most efficient optimization approach is to begin with 2D optimization from random parameter initialization and then follow up with 3D re-optimization. Nevertheless, the latter stage is substantially time- and resource-intensive. Thus, obtaining a desired collection of optimized designs through repeated 3D optimizations is a computational challenge. To address this issue, a machine learning-based regression model is proposed to reduce the computation cost involved in the 3D optimization stage. The regression model correlates the 2D and 3D optimized structural parameters based on a small dataset. Using the predicted design parameters from this model as the initial condition for 3D optimization, the same optima are reached faster. The effectiveness of this approach is demonstrated in the shape optimization-based inverse design of TE0-TE1 mode converters, an important component in mode-division multiplexing applications. The final optimized designs are identical in both approaches, but leveraging a machine learning-based regression model offers a 35% reduction in computation load for the 3D optimization step. The approach provides a more effective means for sampling larger numbers of 3D optimized designs.

Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures.

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

Accurate characterization of complex Bloch modes in optical chain waveguides using real-valued computations

This research presents a highly accurate and easy-to-implement method to characterize the complex Bloch modes propagating along optical chain waveguides with three-dimensional (3D) layered geometries and dispersive negative-epsilon material compositions. The technique combines commercial EM solver results with analytical post-processing to avoid iterative complex root estimation on the complex plane. The proposed methodology is based on the real-valued computations that yield the complex Bloch wavevector with superior accuracy even when both radiation and material losses are present. In addition, we introduce a single unit-cell technique to provide the possibility of dense meshing of 3D geometries when available computational resources are limited. To verify our results, two different plasmonic and dielectric case studies are discussed. The obtained results agree well with numerical and experimental results from the literature. Due to its generality, robustness, and high accuracy, the method is beneficial for studying a large variety of waveguide-based nanophotonic components.

Infrared Resonance Tuning of Nanoslit Antennas with Phase-Change Materials.

Phase-change materials (PCMs) have been established as prime candidates for nonvolatile resonance tuning of nanophotonic components based on a large optical contrast between their amorphous and crystalline states. Recently, the plasmonic PCM In3SbTe2 was introduced, which can be switched from an amorphous dielectric state to a crystalline metallic one over the entire infrared spectral range. While locally switching the PCM around metallic nanorod antennas has already been demonstrated, similar tuning of inverse antenna structures (nanoslits) has not yet been investigated. Here, we demonstrate optical resonance tuning of nanoslit antennas with dielectric and plasmonic PCMs. We compare two geometries with fundamentally different resonance tuning mechanisms: tuning the resonance of aluminum slit antennas by change of the refractive index (dielectric PCM Ge3Sb2Te6), and creating slit-like volumes of amorphous In3SbTe2 and modifying the slit geometry directly (plasmonic PCM In3SbTe2). While the tuning range with the plasmonic PCM is about 3.4 μm and only limited by fabrication, the resonances with the dielectric PCM feature a three times larger quality factor compared to resonances obtained with the plasmonic PCM.

Real-time MRI lungs images revealing using Hybrid feedforward Deep Neural Network and Convolutional Neural Network

This research focused on Real-time MRI lung images that were revealed using three grade processes by manipulating nanophotonics components, mapping by deep learning, machine learning, and pattern recognition. This research is Solving Magnetic resonance imaging of interstitial lung diseases with Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture. The feedforward deep neural network (ffDNN) and Convolutional Neural Network (CNN) techniques are used to Solving Magnetic resonance imaging of interstitial lung diseases on the nanophotonics components, deep learning, and machine learning Platform. The Proposed semiconductor monolithic integration approach employed for bio-Magnetic resonance imaging characterization using photonic crystal “Symptomatic Image Revealing” details of the resonant monolithic. The proposed machine-learning-based approach revealed characterizing multi-parameter design space of nanophotonic components using Nano-optic imagers. The Pattern Recognition for MRI was performed for lower dimensionality. Finally, the Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture for calculating the height and size of scatterers using the inverse design of the meta-optical structure. The temporal resolution assessment of image data pixel size 280x360 hyperspectral imaging temporal resolution is 25, and magnetic resonance imaging temporal resolution is 50. The Image distribution shows that phase shift and transmission are 2.78 degrees and at 95%. The result for the inverse design using CNN returns the efficient inverse design of test data that can be designed according to the required pressure distribution. Wavelength 1000 nanometer to 1600 machine learning method absorbance 40% and ffDNN absorbance 33%.

Arbitrary Phase Optimization Through Adaptively-Scheduled Nanophotonic Inverse Design

Design of integrated photonic devices continues to drive innovation in electro-optical systems for many applications ranging from communications to sensing and computing. Traditional design methods for integrated photonics involve using fundamental physical principles of guided-wave behavior to engineer optical functionalities for specific application requirements. While these traditional approaches may be sufficient for basic functionalities, the set of physically realizable optical capabilities these methods remains limited. Instead, photonic design can be formulated as an inverse problem where the target device functionality is specified, and a numerical optimizer creates the device with appropriate geometrical features within specified constraints. However, even with inverse design methods, achieving arbitrarily-specified phase offsets on-chip remains an important problem to solve for the reliability of interferometry-based nanophotonic applications. In order to address difficulties in achieving simultaneous phase and power optimization in inverse nanophotonic design, in this paper, we develop a set of optimization approaches that can enable user-specified phase differences in single-wavelength and multi-wavelength nanophotonic devices. By specifying phase offset targets for each output, we prevent convergence failures resulting from the changes in the figure of merit and gradient throughout the iterative optimization process. Additionally, by introducing phase-dependent figure of merit terms through an adaptive scheduling approach during the optimization, we accelerate device convergence up to a factor of 4.4 times. Our results outline a clear path towards the optimization of nanophotonic components with arbitrary phase-handling capabilities, with potential applications in a wide variety of integrated photonic systems and platforms.

Reinforcement learning for photonic component design

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers fabricated on an air clad 220 nm silicon on insulator single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm can cover a 150 nm bandwidth with less than 10.2 dB of loss at their lowest point.

Layered Gallium Monosulfide as Phase‐Change Material for Reconfigurable Nanophotonic Components On‐Chip

AbstractThe demand for information processing at ultrahigh speed with large data transmission capacity is continuously rising. Necessary building blocks for on‐chip photonic integrated circuits (PICs) are reconfigurable integrated low‐loss high‐speed modulators and switches. Phase change materials (PCMs) provide unique opportunities for integration into PICs. Here, the investigation of layered gallium monosulfide (GaS) as a novel low‐loss PCM from infrared to optical frequencies is pioneered, with high index contrast (Δn ≈0.5) at the optical telecommunication band. The GaS bandgap switches from 1.5 ± 0.2 eV for the amorphous state to 2.1 ± 0.1 eV for the crystalline state. It is demonstrated that the reversible GaS amorphous‐to‐crystalline phase transition can be operated thermally and by picosecond green (532 nm) laser irradiation. The design of a reconfigurable integrated optical modulator on‐chip based on Mach‐Zehnder Interferometers (MZI) with the GaS PCM cell deposited on one of the arms for application is presented at the telecommunication wavelength of λ = 1310 nm, where the standard single mode optical fiber exhibits zero chromatic dispersion, and at λ = 1550 nm, where a minimum optical loss of 0.22 dB km−1 is obtained. This opens the route to applications such as reconfigurable modulators, beam steering using phase modulation, and photonic neural networks.

Temperature invariant metasurfaces

Abstract Thermal effects are well known to influence the electronic and optical properties of materials through several physical mechanisms and are the basis for various optoelectronic devices. The thermo-optic (TO) effect, the refractive index variation with temperature (dn/dT), is one of the most common mechanisms used for tunable optical devices, including integrated optical components, metasurfaces, and nano-antennas. However, when a static and fixed operation is required, i.e., temperature invariant performance – this effect becomes a drawback and may lead to undesirable behavior through drifting of the resonance frequency, amplitude, or phase, as the operating temperature varies over time. In this work, we present a systematic approach to mitigate thermally induced optical fluctuations in nanophotonic devices. By using hybrid subwavelength resonators composed from two materials with opposite TO dispersions (dn/dT &lt; 0 and dn/dT &gt; 0), we are able to compensate for TO shifts and engineer nanophotonic components with zero effective TO coefficient (dn eff/dT ≈ 0). We demonstrate temperature invariant resonant frequency, amplitude, and phase response in meta-atoms and metasurfaces operating across a wide temperature range and broad spectral band. Our results highlight a path towards temperature invariant nanophotonics, which can provide constant and stable optical response across a wide range of temperatures and be applied to a plethora of optoelectronic devices. Controlling the sign and magnitude of TO dispersion extends the capabilities of light manipulation and adds another layer to the toolbox of optical engineering in nanophotonic systems.

Engineering Quantum Nanophotonic Components from Hexagonal Boron Nitride

AbstractIntegrated quantum photonics (IQP) provides a path to practical, scalable quantum computation, communications and information processing. Realization of an IQP platform requires controlled engineering of many nanophotonic components. However, the range of materials for monolithic platforms is limited by the simultaneous need for high‐quality quantum light sources, high optical performance, and availability of scalable nanofabrication techniques. Here, the fabrication of IQP components from the recently emerged material hexagonal boron nitride (hBN), including tapered waveguides, microdisks, and 1D and 2D photonic crystal cavities, is demonstrated. Resonators with quality factors greater than 4000 are achieved, and proof‐of‐principle complex, free‐standing IQP circuitry fabricated from single‐crystal hBN is engineered. The results show the potential of hBN for scalable integrated quantum technologies.

All‐Dielectric Huygens’ Meta‐Waveguides for Resonant Integrated Photonics

AbstractThe growing maturity of nanofabrication technology has recently enabled the deployment of high‐quality subwavelength nanostructures on photonic chips. Combining existing photonic waveguide technology with the paradigms adapted from metamaterials opens new avenues towards unprecedented control of guided light waves. However, developing new functionalities while preserving efficiencies and offering compatibility with current technology remains a major challenge in on‐chip nanophotonics. Here, a novel silicon nanophotonic waveguide comprising a chain of resonantly forward scattering nanoparticles empowered by spectrally overlapping electric and magnetic dipolar Mie‐type resonances is proposed and demonstrated. The propagation loss of the meta‐waveguides in the telecom spectral range is as low as 0.4 dB mm−1, exceeding the current record for Mie‐resonant waveguides by more than an order of magnitude. Furthermore, the meta‐waveguides support a negative group index over a broad spectral range of 60 nm and regions of vanishing and anomalous dispersion within the transmission band. Finally, it is shown that meta‐waveguide topologies can implement compact resonance‐protected waveguide bends and efficient splitters within just 320 nm propagation length. This work addresses the fundamental challenges of miniaturization, dispersion, and scattering control in integrated photonics and opens new opportunities for enhancing light–matter interactions, interfacing nanophotonic components, and developing nonlinear, ultrafast, and quantum optics resonant on‐chip devices.

Improving the performance of all-optical logic gates based on metal-insulator-metal heterogeneous plasmonic waveguide by Kerr nonlinearity effect

In this paper, the nonlinear effect of Kerr on increasing the efficiency of multi-channel all-optical metal-insulator-metal logic gates with heterogeneous metals is investigated. In this study, linear interference between surface plasmonic polariton modes has been employed. The performance of OR, AND, XOR and NOR gates is numerically analyzed by the finite element method. A structure with fixed physical dimensions can manage several basic logic functions. By applying optical signals to various input ports in the proposed structure, we can implement OR, AND, and XOR logic functions. The NOR logic gate can be established by the addition of a control optical signal. It has been shown that the nonlinear material of Kerr increases the difference of field intensity between logic levels of "0" and "1" that are 77 V/m for OR gate, 133 V/m for AND gate, 89 V/m for XOR gate, and 138 V/m for NOR gate. Also, simulation results show the minimum extinction ratio (ER) of 33.5, 36.4, 45.4, and 42.4 dB for OR, AND, XOR, and NOR logic gates, respectively. The proposed all-optical logic gate has a simple and compact arrangement and it can be applied to many nanophotonic components used in optical communication networks.

Machine Learning in Interpolation and Extrapolation for Nanophotonic Inverse Design.

The algorithmic design of nanophotonic structures promises to significantly improve the efficiency of nanophotonic components due to the strong dependence of electromagnetic function on geometry and the unintuitive connection between structure and response. Such approaches, however, can be highly computationally intensive and do not ensure a globally optimal solution. Recent theoretical results suggest that machine learning techniques could address these issues as they are capable of modeling the response of nanophotonic structures at orders of magnitude lower time per result. In this work, we explore the utilization of artificial neural network (ANN) techniques to improve the algorithmic design of simple absorbing nanophotonic structures. We show that different approaches show various aptitudes in interpolation versus extrapolation, as well as peak performances versus consistency. Combining ANNs with classical machine learning techniques can outperform some standard ANN techniques for forward design, both in terms of training speed and accuracy in interpolation, but extrapolative performance can suffer. Networks pretrained on general image classification perform well in predicting optical responses of both interpolative and extrapolative structures, with very little additional training time required. Furthermore, we show that traditional deep neural networks are able to perform significantly better in extrapolation than more complicated architectures using convolutional or autoencoder layers. Finally, we show that such networks are able to perform extrapolation tasks in structure generation to produce structures with spectral responses significantly outside those of the structures on which they are trained.

Micromanufacturing of Geometrically and Dimensionally Precise Molecular Single‐Crystal Photonic Microresonators via Focused Ion Beam Milling

AbstractHighly reproducible manufacturing of organic optical crystals with well‐defined geometry and dimension is important to realize industrially relevant all‐organic microelectronic and nanophotonic components and photonic integrated circuits. Here, programmed shape and size alteration of square‐shaped perylene crystal resonators into circular disk and rectangular geometries is demonstrated using the focused‐ion‐beam milling technique. The fabricated smaller sized circular disk and rectangular crystal resonators display shape‐ and size‐dependent optical modes. Due to generality and high reproducibility, this processing technique can be also extended to other organic crystals to create optical components suitable for commercial nanophotonic device applications.

Asymmetric parametric generation of images with nonlinear dielectric metasurfaces

Subwavelength dielectric resonators assembled into metasurfaces have become a versatile tool for miniaturising optical components approaching the nanoscale. An important class of metasurface functionalities is associated with asymmetry in both generation and transmission of light with respect to reversals of the positions of emitters and receivers. Nonlinear light-matter interaction in metasurfaces offers a promising pathway towards miniaturisation of the asymmetric control of light. Here we demonstrate asymmetric parametric generation of light in nonlinear metasurfaces. We assemble dissimilar nonlinear dielectric resonators into translucent metasurfaces that produce images in the visible spectral range being illuminated by infrared radiation. By design, the metasurfaces produce different and completely independent images for the reversed direction of illumination, that is when the positions of the infrared emitter and the visible light receiver are exchanged. Nonlinearity-enabled asymmetric control of light by subwavelength resonators paves the way towards novel nanophotonic components via dense integration of large quantities of nonlinear resonators into compact metasurface designs.

Engineering a mechanically stable hybrid photonic crystal cavity coupled to color defects in diamond

Wavelength scale photonic, high-quality factor (Q-factor), cavities are crucial for enhancing light–matter interaction. Such on-chip hybrid devices could potentially provide a route towards scalable quantum technologies with color defects in diamond. A variety of designs for photonic crystal cavities have been proposed; however, the challenging and multi-step fabrication processes required for such designs limit the experimentally observed Q-factors in addition to significant radiation loss. One possible way to minimize the radiation loss in a one-dimensional (1D) photonic crystal cavity is by introducing a so-called Gaussian defect region around the cavity. In this work, we propose a versatile approach to design a Gaussian defect region by changing the lattice parameter in a 1D photonic crystal. Further, to circumvent the problem of creating freestanding cavities for achieving high-Q factor in a 1D photonic crystal cavity, we propose a novel hybrid diamond-titanium dioxide (TiO2) based materials for color defects in diamond, in particular the nitrogen-vacancy (NV−) center. Our proposed mechanically stable and high-Q cavity could be crucial for on-chip integration of different nanophotonic components for chip-scale photonic devices. We show via simulations that Q-factor 105 can be achieved with the device.

Nanostructured In3SbTe2 antennas enable switching from sharp dielectric to broad plasmonic resonances

Abstract Phase-change materials (PCMs) allow for non-volatile resonance tuning of nanophotonic components. Upon switching, they offer a large dielectric contrast between their amorphous and crystalline phases. The recently introduced “plasmonic PCM” In3SbTe2 (IST) additionally features in its crystalline phase a sign change of its permittivity over a broad infrared spectral range. While optical resonance switching in unpatterned IST thin films has been investigated before, nanostructured IST antennas have not been studied, yet. Here, we present numerical and experimental investigations of nanostructured IST rod and disk antennas. By crystallizing the IST with microsecond laser pulses, we switched individual antennas from narrow dielectric to broad plasmonic resonances. For the rod antennas, we demonstrated a resonance shift of up to 1.2 µm (twice the resonance width), allowing on/off switching of plasmonic resonances with a contrast ratio of 2.7. With the disk antennas, we realized an increase of the resonance width by more than 800% from 0.24 µm to 1.98 µm while keeping the resonance wavelength constant. Further, we demonstrated intermediate switching states by tuning the crystallization depth within the resonators. Our work empowers future design concepts for nanophotonic applications like active spectral filters, tunable absorbers, and switchable flat optics.