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Mario Miscuglio

Publications and source records attributed to Mario Miscuglio.

At least 19 recordsLinked to original sources
arXiv2022

Electrical Programmable Multi-Level Non-volatile Photonic Random-Access Memory

Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multi-state electrically-programmed low-loss non-volatile photonic memory based on a broadband transparent phase change material (Ge2Sb2Se5, GSSe) with ultra-low absorption in the amorphous state. A zero-static-power and electrically-programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/{\mu}m and an ultra-low insertion loss of total 0.12 dB for a 4-bit memory showing a 100x improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual micro-heaters validating performance tradeoffs. Experimentally we demonstrate a half-a million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example.

physics.optics↗
arXiv2021

High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.

cs.LG↗
arXiv2021

100 GHz Micrometer compact broadband Monolithic ITO Mach Zehnder Interferometer Modulator enabling 3500 times higher Packing Density

Electro-optic modulators provide a key function in optical transceivers and increasingly in photonic programmable Application Specific Integrated Circuits (ASICs) for machine learning and signal processing. However, both foundry ready silicon based modulators and conventional material based devices utilizing Lithium niobate fall short in simultaneously providing high chip packaging density and fast speed. Current driven ITO based modulators have the potential to achieve both enabled by efficient light matter interactions. Here, we introduce micrometer compact Mach Zehnder Interferometer (MZI) based modulators capable of exceeding 100 GHz switching rates. Integrating ITO thin films atop a photonic waveguide, spectrally broadband, and compact MZI phase shifter. Remarkably, this allows integrating more than 3500 of these modulators within the same chip area as only one single silicon MZI modulator. The modulator design introduced here features a holistic photonic, electronic, and RF-based optimization and includes an asymmetric MZI tuning step to optimize the Extinction Ratio (ER) to Insertion Loss (IL) and dielectric thickness sweep to balance the tradeoffs between ER and speed. Driven by CMOS compatible bias voltage levels, this device is the first to address next generation modulator demands for processors of the machine intelligence revolution, in addition to the edge and cloud computing demands as well as optical transceivers alike.

physics.optics↗
arXiv2021

Integrated ultra-high-performance graphene optical modulator

With the increasing need for large volumes of data processing, transport, and storage, optimizing the trade-off between high-speed and energy consumption in today's optoelectronic devices is getting increasingly difficult. Heterogeneous material integration into Silicon- and Nitride-based photonics has showed high-speed promise, albeit at the expense of millimeter- to centimeter-scale footprints. The hunt for an electro-optic modulator that combines high speed, energy efficiency, and compactness to support high component density on-chip continues. Using a double-layer graphene optical modulator integrated on a Silicon photonics platform, we are able to achieve 60 GHz speed (3 dB roll-off), micrometer compactness, and efficiency of 2.25 fJ/bit in this paper. The electro-optic response is boosted further by a vertical distributed-Bragg-reflector cavity, which reduces the driving voltage by about 40 times while maintaining a sufficient modulation depth (5.2 dB/V). Modulators that are small, efficient, and quick allow high photonic chip density and performance, which is critical for signal processing, sensor platforms, and analog- and neuromorphic photonic processors.

physics.optics↗
arXiv2021

Prospects and applications of photonic neural networks

Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.

cs.ET↗
arXiv2021

All-Chalcogenide Programmable All-Optical Deep Neural Networks

Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic conversions could alleviate these shortcomings. However, an all-optical nonlinear activation function, which is a vital building block for optical neural networks, needs to be developed efficiently on-chip. Here, we introduce and demonstrate both optical synapse weighting and all-optical nonlinear thresholding using two different effects in a chalcogenide material photonic platform. We show how the structural phase transitions in a wide-bandgap phase-change material enables storing the neural network weights via non-volatile photonic memory, whilst resonant bond destabilisation is used as a nonlinear activation threshold without changing the material. These two different transitions within chalcogenides enable programmable neural networks with near-zero static power consumption once trained, in addition to picosecond delays performing inference tasks not limited by wire charging that limit electrical circuits; for instance, we show that nanosecond-order weight programming and near-instantaneous weight updates enable accurate inference tasks within 20 picoseconds in a 3-layer all-optical neural network. Optical neural networks that bypass electro-optic conversion altogether hold promise for network-edge machine learning applications where decision-making in real-time are critical, such as for autonomous vehicles or navigation systems such as signal pre-processing of LIDAR systems.

physics.optics↗
arXiv2020

Quantifying information via Shannon entropy in spatially structured optical beams

While information is ubiquitously generated, shared, and analyzed in a modern-day life, there is still some controversy around the ways to asses the amount and quality of information inside a noisy optical channel. A number of theoretical approaches based on, e.g., conditional Shannon entropy and Fisher information have been developed, along with some experimental validations. Some of these approaches are limited to a certain alphabet, while others tend to fall short when considering optical beams with non-trivial structure, such as Hermite-Gauss, Laguerre-Gauss and other modes with non-trivial structure. Here, we propose a new definition of classical Shannon information via the Wigner distribution function, while respecting the Heisenberg inequality. Following this definition, we calculate the amount of information in a Gaussian, Hermite-Gaussian, and Laguerre-Gaussian laser modes in juxtaposition and experimentally validate it by reconstruction of the Wigner distribution function from the intensity distribution of structured laser beams. We experimentally demonstrate the technique that allows to infer field structure of the laser beams in singular optics to assess the amount of contained information. Given the generality, this approach of defining information via analyzing the beam complexity is applicable to laser modes of any topology that can be described by 'well-behaved' functions. Classical Shannon information defined in this way is detached from a particular alphabet, i.e. communication scheme, and scales with the structural complexity of the system. Such a synergy between the Wigner distribution function encompassing the information in both real and reciprocal space, and information being a measure of disorder, can contribute into future coherent detection algorithms and remote sensing.

physics.optics↗
arXiv2020

Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators

Low latency, high throughput inference on Convolution Neural Networks (CNNs) remains a challenge, especially for applications requiring large input or large kernel sizes. 4F optics provides a solution to accelerate CNNs by converting convolutions into Fourier-domain point-wise multiplications that are computationally 'free' in optical domain. However, existing 4F CNN systems suffer from the all-positive sensor readout issue which makes the implementation of a multi-channel, multi-layer CNN not scalable or even impractical. In this paper we propose a simple channel tiling scheme for 4F CNN systems that utilizes the high resolution of 4F system to perform channel summation inherently in optical domain before sensor detection, so the outputs of different channels can be correctly accumulated. Compared to state of the art, channel tiling gives similar accuracy, significantly better robustness to sensing quantization (33\% improvement in required sensing precision) error and noise (10dB reduction in tolerable sensing noise), 0.5X total filters required, 10-50X+ throughput improvement and as much as 3X reduction in required output camera resolution/bandwidth. Not requiring any additional optical hardware, the proposed channel tiling approach addresses an important throughput and precision bottleneck of high-speed, massively-parallel optical 4F computing systems.

cs.ET↗
arXiv2020

Massively Parallel Amplitude-Only Fourier Neural Network

Machine-intelligence has become a driving factor in modern society. However, its demand outpaces the underlying electronic technology due to limitations given by fundamental physics such as capacitive charging of wires, but also by system architecture of storing and handling data, both driving recent trends towards processor heterogeneity. Here we introduce a novel amplitude-only Fourier-optical processor paradigm capable of processing large-scale ~(1,000 x 1,000) matrices in a single time-step and 100 microsecond-short latency. Conceptually, the information-flow direction is orthogonal to the two-dimensional programmable-network, which leverages 10^6-parallel channels of display technology, and enables a prototype demonstration performing convolutions as pixel-wise multiplications in the Fourier domain reaching peta operations per second throughputs. The required real-to-Fourier domain transformations are performed passively by optical lenses at zero-static power. We exemplary realize a convolutional neural network (CNN) performing classification tasks on 2-Megapixel large matrices at 10 kHz rates, which latency-outperforms current GPU and phase-based display technology by one and two orders of magnitude, respectively. Training this optical convolutional layer on image classification tasks and utilizing it in a hybrid optical-electronic CNN, shows classification accuracy of 98% (MNIST) and 54% (CIFAR-10). Interestingly, the amplitude-only CNN is inherently robust against coherence noise in contrast to phase-based paradigms and features an over 2 orders of magnitude lower delay than liquid crystal-based systems. Beyond contributing to novel accelerator technology, scientifically this amplitude-only massively-parallel optical compute-paradigm can be far-reaching as it de-validates the assumption that phase-information outweighs amplitude in optical processors for machine-intelligence.

eess.IV↗
arXiv2020

Analog Computing with Metatronic Circuits

Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. Here, we explore a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting control of deposition technique through process parameters, we demonstrate the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements. Our nano-optical analog processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.

physics.optics↗
arXiv2020

Heterogeneously Integrated ITO Plasmonic Mach-Zehnder Interferometric Modulator on SOI

Densely integrated active photonics is key for next generation on-chip networks for addressing both footprint and energy budget concerns. However, the weak light-matter interaction in traditional active Silicon optoelectronics mandates rather sizable device lengths. The ideal active material choice should avail high index modulation while being easily integrated into Silicon photonics platforms. Indium tin oxide (ITO) offers such functionalities and has shown promising modulation capacity recently. Interestingly, the nanometer-thin unity-strong index modulation of ITO synergistically combines the high group-index in hybrid plasmonic with nanoscale optical modes. Following this design paradigm, here, we demonstrate a spectrally broadband, GHz-fast Mach-Zehnder interferometric modulator, exhibiting a high efficiency signified by a miniscule VpL of 95 Vum, deploying an one-micrometer compact electrostatically tunable plasmonic phase-shifter, based on heterogeneously integrated ITO thin films into silicon photonics. Furthermore we show, that this device paradigm enables spectrally broadband operation across the entire telecommunication near infrared C-band. Such sub-wavelength short efficient and fast modulators monolithically integrated into Silicon platform open up new possibilities for high-density photonic circuitry, which is critical for high interconnect density of photonic neural networks or applications in GHz-fast optical phased-arrays, for example.

physics.app-ph↗
arXiv2020

Induced Homomorphism Kirchhoffs Law in Photonics

When solving, modelling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospect to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Although, the distributed nature of photonic platforms leads to the absence of an equivalent approximation to Kirchhoffs law, the main principle used for representing physical systems using circuits. Here we argue that in absence of a straightforward parallelism and homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoffs law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. We experimentally demonstrate generating in one-shot discrete solutions of a Laplace partial differential equation, with an accuracy above 95% relative to commercial solvers, for an arbitrary set of boundary conditions. Our photonic engine can provide a route to achieve chip-scale, fast (10s of ps), and integrable reprogrammable accelerators for the next generation hybrid high performance computing.

physics.optics↗
arXiv2020

Giant-Shell CdSe/CdS Nanocrystals: Exciton Coupling to Shell Phonons Investigated by Resonant Raman Spectroscopy

The interaction between excitons and phonons in semiconductor nanocrystals plays a crucial role in the exciton energy spectrum and dynamics, and thus in their optical properties. We investigate the exciton2 phonon coupling in giant-shell CdSe/CdS core-shell nanocrystals via resonant Raman spectroscopy. The Huang-Rhys parameter is evaluated by the intensity ratio of the longitudinal-optical (LO) phonon of CdS with its first multiscattering (2LO) replica. We used four different excitation wavelengths in the range from the onset of the CdS shell absorption to well above the CdS shell band edge to get insight into resonance effects of the CdS LO phonon with high energy excitonic transitions. The isotropic spherical giant-shell nanocrystals show consistently stronger exciton-phonon coupling as compared to the anisotropic rod-shaped dot-in-rod (DiR) architecture, and the 2LO/LO intensity ratio decreases for excitation wavelengths approaching the CdS band edge. The strong exciton-phonon coupling in the spherical giant-shell nanocrystals can be related to the delocalization of the electronic wave functions. Furthermore, we observe the radial breathing modes of the GS nanocrystals and their overtones by ultralow frequency Raman spectroscopy with nonresonant excitation, using laser energies well below the band gap of the heteronanocrystals, and highlight the differences between higher order

physics.chem-ph↗
arXiv2020

Planar Aperiodic Arrays as Metasurfaces for Optical Near-Field Patterning

Plasmonic metasurfaces have spawned the field of flat optics using nanostructured planar metallic or dielectric surfaces that can replace bulky optical elements and enhance the capabilities of traditional far-field optics. Furthermore, the potential of flat optics can go far beyond far-field modulation, and can be exploited for functionality in the near-field itself. Here, we design metasurfaces based on aperiodic arrays of plasmonic Au nanostructures for tailoring the optical near-field in the visible and near-infrared spectral range. The basic element of the arrays is a rhomboid that is modulated in size, orientation and position to achieve the desired functionality of the micron-size metasurface structure. Using two-photon-photoluminescence as a tool to probethe near-field profiles in the plane of the metasurfaces, we demonstrate the molding of light into different near-field intensity patterns and active pattern control via the far-field illumination. Finite element method simulations reveal that the near-field modulation occurs via a combination of the plasmonic resonances of the rhomboids and field enhancement in the nanoscale gaps in between the elements. This approach enables optical elements that can switch the near-field distribution across the metasurface via wavelength and polarization of the incident far-field light, and provides pathways for light matter interaction in integrated devices.

physics.app-ph↗
arXiv2020

Photonic tensor cores for machine learning

With an ongoing trend in computing hardware towards increased heterogeneity, domain-specific co-processors are emerging as alternatives to centralized paradigms. The tensor core unit (TPU) has shown to outperform graphic process units by almost 3-orders of magnitude enabled by higher signal throughout and energy efficiency. In this context, photons bear a number of synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non van-Neumann architectures. While several photonic neural network designs have been explored, a photonic TPU to perform matrix vector multiplication and summation is yet outstanding. Here we introduced an integrated photonics-based TPU by strategically utilizing a) photonic parallelism via wavelength division multiplexing, b) high 2 Peta-operations-per second throughputs enabled by 10s of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and c) zero power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show that the performance of this 8-bit photonic TPU can be 2-3 orders higher compared to an electrical TPU whilst featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.

cond-mat.dis-nn↗
arXiv2019

A Guide for Material and Design Choices for Electro-Optic Modulators and recent 2D-Material Silicon Modulator Demonstrations

Electro-optic modulation performs a technological relevant functionality such as for communication, beam steering, or neuromorphic computing through providing the nonlinear activation function of a perceptron. Wile Silicon photonics enabled the integration and hence miniaturization of optoelectronic devices, the weak electro-optic performance of Silicon renders these modulators to be bulky and power-hungry compared to a single switch functionality known from electronics. To gain deeper insights into the physics and operation of modulators hetero-generous integration of emerging electro-optically active materials could enable separating light passive and low-loss light routing from active light manipulation. Here we discuss and review our recent work on a) fundamental performance vectors of electro-optic modulators, and b) showcase recent development of heterogeneous-integrated emerging EO materials into Si-photonics to include an ITO-based MZM, a Graphene hybrid-plasmon and the first TMD-MRR modulator using a microring resonator. Our results indicate a viable path for energy efficient and compact Silicon photonic based modulators.

physics.app-ph↗
arXiv2019

OE-CAM: A Hybrid Opto-Electronic Content Addressable Memory

A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10s of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10-100s ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceivers responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors.

physics.app-ph↗