Publications

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Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

Vertically aligned nanocomposites (VANs) are unique thin films with vertical nanostructures embedded in a matrix material, allowing for the integration of two distinct materials. These nanocomposites offer novel combined physical properties, such as nanocomposite-based multiferroics and strongly coupled physical properties, such as magneto-optic coupling. Much work has been conducted in exploring different two-phase combinations and various processing conditions to achieve novel tunable properties that cannot be obtained by any single-phase material alone. In this work, the target configuration effects are explored for the growth of LaFeO3-CoNi2O4 VANs. Both mixed and pie-shaped targets are utilized to compare the target configuration effects on the phase separation, morphology tuning, and their resulting physical properties, including optical and magnetic properties. This work suggests that the target configuration is another important parameter for achieving the desired VAN morphology and can be used to design different two-phase VANs with tailorable properties.

Photonic computing has the potential to harness the full degrees of freedom (DOFs) of the light field, including the wavelength, spatial mode, spatial location, phase quadrature, and polarization, to achieve a higher level of computing parallelism and scalability than digital electronic processors. While multiplexing using the wavelength and other DOFs can be readily integrated on silicon photonics platforms with compact footprints, conventional mode-division multiplexed (MDM) photonic designs occupy areas exceeding tens to hundreds of microns for a few spatial modes, significantly limiting their scalability. Here, we utilize inverse design to demonstrate an ultracompact photonic computing core that calculates vector dot products based on MDM coherent mixing. Our dot-product core integrates the functionalities of two-mode multiplexers and one multimode coherent mixer within a nominal footprint of 5 μm x 3 μm . We have experimentally demonstrated computing examples on the fabricated dot-product core, including complex number multiplication and motion estimation using optical flow. The compact dot-product core design enables large-scale on-chip integration in a parallel photonic computing primitive cluster for high-throughput scientific computing and computer vision tasks.

The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.

Magnetic and ferroelectric oxide thin films have long been studied for their applications in electronics, optics, and sensors. The properties of these oxide thin films are highly dependent on the film growth quality and conditions. To maximize the film quality, epitaxial oxide thin films are frequently grown on single-crystal oxide substrates such as strontium titanate (SrTiO3) and lanthanum aluminate (LaAlO3) to satisfy lattice matching and minimize defect formation. However, these single-crystal oxide substrates cannot readily be used in practical applications due to their high cost, limited availability, and small wafer sizes. One leading solution to this challenge is film transfer. In this demonstration, a material from a new class of multiferroic oxides is selected, namely bismuth-based layered oxides, for the transfer. A water-soluble sacrificial layer of Sr3Al2O6 is inserted between the oxide substrate and the film, enabling the release of the film from the original substrate onto a polymer support layer. The films are transferred onto new substrates of silicon and lithium niobate (LiNbO3) and the polymer layer is removed. These substrates allow for the future design of electronic and optical devices as well as sensors using this new group of multiferroic layered oxide films.

Precise control of light-matter interactions at the nanoscale lies at the heart of nanophotonics. However, experimental examination at this length scale is challenging since the corresponding electromagnetic near-field is often confined within volumes below the resolution of conventional optical microscopy. In semiconductor nanophotonics, electromagnetic fields are further restricted within the confines of individual subwavelength resonators, limiting access to critical light-matter interactions in these structures. In this work, we demonstrate that photoelectron emission microscopy (PEEM) can be used for polarization-resolved near-field spectroscopy and imaging of electromagnetic resonances supported by broken-symmetry silicon metasurfaces. We find that the photoemission results, enabled through an in situ potassium surface layer, are consistent with full-wave simulations and far-field reflectance measurements across visible and near-infrared wavelengths. In addition, we uncover a polarization-dependent evolution of collective resonances near the metasurface array edge taking advantage of the far-field excitation and full-field imaging of PEEM. Here, we deduce that coupling between eight resonators or more establishes the collective excitations of this metasurface. All told, we demonstrate that the high-spatial resolution hyperspectral imaging and far-field illumination of PEEM can be leveraged for the metrology of collective, non-local, optical resonances in semiconductor nanophotonic structures.

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We present optoelectronic characterization of ntype silicon pixels with a suite of plasmonic designs intended to generate and detect electron-hole pairs from incident 1550 nm photons.

We present optoelectronic characterization of ntype silicon pixels with a suite of plasmonic designs intended to generate and detect electron-hole pairs from incident 1550 nm photons.

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Aperture near-field microscopy and spectroscopy (a-SNOM) enables the direct experimental investigation of subwavelength-sized resonators by sampling highly confined local evanescent fields on the sample surface. Despite its success, the versatility and applicability of a-SNOM is limited by the sensitivity of the aperture probe, as well as the power and versatility of THz sources used to excite samples. Recently, perfectly absorbing photoconductive metasurfaces have been integrated into THz photoconductive antenna detectors, enhancing their efficiency and enabling high signal-to-noise ratio THz detection at significantly reduced optical pump powers. Here, we discuss how this technology can be applied to aperture near-field probes to improve both the sensitivity and potentially spatial resolution of a-SNOM systems. In addition, we explore the application of photoconductive metasurfaces also as near-field THz sources, providing the possibility of tailoring the beam profile, polarity and phase of THz excitation. Photoconductive metasurfaces therefore have the potential to broaden the application scope of aperture near-field microscopy to samples and material systems which currently require improved spatial resolution, signal-to-noise ratio, or more complex excitation conditions.

We demonstrate the use of low-temperature grown GaAs (LT-GaAs) metasurface as an ultrafast photoconductive switching element gated with 1550 nm laser pulses. The metasurface is designed to enhance a weak two-step photon absorption at 1550 nm, enabling THz pulse detection.

All-dielectric metasurfaces have recently led to a paradigm shift in nonlinear optics as they allow for circumventing the phase matching constraints of bulk crystals and offer high nonlinear conversion efficiencies when normalized by the light-matter interaction volume. Unlike bulk crystals, in all-dielectric metasurfaces nonlinear conversion efficiencies primarily rely on the material nonlinearity, field enhancements, and the modal overlaps, therefore most efforts to date have only focused on utilizing these degrees of freedom. In this work, we demonstrate that for second-harmonic generation in all-dielectric metasurfaces, an additional degree of freedom is the control of the polarity of the nonlinear susceptibility. We demonstrate that semiconductor heterostructures that support resonant nonlinearities based on quantum-engineered intersubband transitions provide this new degree of freedom. We can flip and control the polarity of the nonlinear susceptibility of the dielectric medium along the growth direction and couple it to the Mie-type photonic modes. Here we demonstrate that engineering the χ(2) polarity in the meta-atom enables the control of the second-harmonic radiation pattern and conversion efficiency. Our results therefore open a new direction for engineering and optimizing second-harmonic generation using all-dielectric intersubband nonlinear metasurfaces.

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Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

Enhancing the efficiency of second-harmonic generation using all-dielectric metasurfaces to date has mostly focused on electromagnetic engineering of optical modes in the meta-atom. Further advances in nonlinear conversion efficiencies can be gained by engineering the material nonlinearities at the nanoscale, however this cannot be achieved using conventional materials. Semiconductor heterostructures that support resonant nonlinearities using quantum engineered intersubband transitions can provide this new degree of freedom. By simultaneously optimizing the heterostructures and meta-atoms, we experimentally realize an all-dielectric polaritonic metasurface with a maximum second-harmonic generation power conversion factor of 0.5 mW/W2 and power conversion efficiencies of 0.015% at nominal pump intensities of 11 kW/cm2. These conversion efficiencies are higher than the record values reported to date in all-dielectric nonlinear metasurfaces but with 3 orders of magnitude lower pump power. Our results therefore open a new direction for designing efficient nonlinear all-dielectric metasurfaces for new classical and quantum light sources.

We developed a simplistic physics-based model of an all-optical neural network that mimics the encoder part of an autoencoder neural network for image compression. Our approach relies on the generation of a MATLAB-based model for both data compression and decompression and utilizes MATLAB's built-in autoencoder networks in combination with simple propagation of optical fields between layers constituting phase elements via Fourier transform. We optimize the phase elements using the particle swarm optimization technique and using our model, we demonstrate a compression ratio of 25% for 2828-pixel input images containing numeric digits from 0 to 9.

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