arXiv subjects

Volker J. Sorger

Publications and source records attributed to Volker J. Sorger.

At least 37 records · Page 2Linked to original sources

arXiv2021

On-chip Nanophotonic Broadband Wavelength Detector with 2D-Electron Gas

Miniaturized, low-cost wavelength detectors are gaining enormous interest as we step into the new age of photonics. Incompatibility with integrated circuits or complex fabrication requirement in most of the conventionally used filters necessitates the development of a simple, on-chip platform for easy-to-use wavelength detection system. Also, intensity fluctuations hinder precise, noise free detection of spectral information. Here we propose a novel approach of utilizing wavelength sensitive photocurrent across semiconductor heterojunctions to experimentally validate broadband wavelength detection on an on-chip platform with simple fabrication process. The proposed device utilizes linear frequency response of internal photoemission via 2-D electron gas in a ZnO based heterojunction along with a reference junction for coherent common mode rejection. We report sensitivity of 0.96 uA/nm for a broad wavelength-range of 280 nm from 660-940 nm. Simple fabrication process, efficient intensity noise cancelation along with heat resistance and radiation hardness of ZnO makes the proposed platform simple, low-cost and efficient alternative for several applications such as optical spectrometers, sensing and IOTs.

physics.optics↗

arXiv2021

Roadmap on Integrated Quantum Photonics

Integrated photonics is at the heart of many classical technologies, from optical communications to biosensors, LIDAR, and data center fiber interconnects. There is strong evidence that these integrated technologies will play a key role in quantum systems as they grow from few-qubit prototypes to tens of thousands of qubits. The underlying laser and optical quantum technologies, with the required functionality and performance, can only be realized through the integration of these components onto quantum photonic integrated circuits (QPICs) with accompanying electronics. In the last decade, remarkable advances in quantum photonic integration and a dramatic reduction in optical losses have enabled benchtop experiments to be scaled down to prototype chips with improvements in efficiency, robustness, and key performance metrics. The reduction in size, weight, power, and improvement in stability that will be enabled by QPICs will play a key role in increasing the degree of complexity and scale in quantum demonstrations. In the next decade, with sustained research, development, and investment in the quantum photonic ecosystem (i.e. PIC-based platforms, devices and circuits, fabrication and integration processes, packaging, and testing and benchmarking), we will witness the transition from single- and few-function prototypes to the large-scale integration of multi-functional and reconfigurable QPICs that will define how information is processed, stored, transmitted, and utilized for quantum computing, communications, metrology, and sensing. This roadmap highlights the current progress in the field of integrated quantum photonics, future challenges, and advances in science and technology needed to meet these challenges.

quant-ph↗

arXiv2021

Highly Accurate, Reliable and Non-Contaminating Two-Dimensional Material Transfer System

The exotic properties of two-dimensional (2D) materials and 2D heterostructures, built by forming heterogeneous multi-layered stacks, have been widely explored across a number of subject matters following the goal to invent, design, and improve applications enabled by 2D materials. To successfully harvest these unique properties effectively and increase the yield of manufacturing 2D material-based devices for achieving reliable and repeatable results is the current challenge. The scientific community has introduced various experimental transfer systems explained in detail for exfoliated 2D materials, however, the field lacks statistical analysis and the capability of producing a transfer technique enabling; i) high transfer precision and yield, ii) cross-contamination free transfer, iii) multi-substrate transfer, and iv) rapid prototyping without wet chemistry. Here we introduce a novel 2D material deterministic transfer system and experimentally show its high accuracy, reliability, repeatability, and non-contaminating transfer features by demonstrating fabrication of 2D material-based optoelectronic devices featuring novel device physics and unique functionality. Such rapid and material-near prototyping capability can accelerate not only layered material science in discovery but also engineering innovations.

cond-mat.mes-hall↗

arXiv2021

An ITO Graphene hybrid integrated absorption modulator on Si-photonics for neuromorphic nonlinear activation

The high demand for machine intelligence of doubling every three months is driving novel hardware solutions beyond charging of electrical wires given a resurrection to application specific integrated circuit (ASIC)-based accelerators. These innovations include photonic ASICs (P-ASIC) due to prospects of performing optical linear (and also nonlinear) operations, such as multiply-accumulate for vector matrix multiplications or convolutions, without iterative architectures. Such photonic linear algebra enables picosecond delay when photonic integrated circuits are utilized, via on-the-fly mathematics. However, the neurons full function includes providing a nonlinear activation function, knowns as thresholding, to enable decision making on inferred data. Many P-ASIC solutions performing this nonlinearity in the electronic domain, which brings challenges in terms of data throughput and delay, thus breaking the optical link and introducing increased system complexity via domain crossings. This work follows the notion of utilizing enhanced light-matter-interactions to provide efficient, compact, and engineerable electro-optic neuron nonlinearity. Here, we introduce and demonstrate a novel electro-optic device to engineer the shape of this optical nonlinearity to resemble a rectifying linear unit (ReLU) - the most-commonly used nonlinear activation function in neural networks. We combine the counter-directional transfer functions from heterostructures made out of two electro-optic materials to design a diode-like nonlinear response of the device. Integrating this nonlinearity into a photonic neural network, we show how the electrostatics of this thresholders gating junction improves machine learning inference accuracy and the energy efficiency of the neural network.

physics.app-ph↗

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↗

arXiv2021

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

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

Strain Induced Modulation of Local Transport of 2D Materials at the Nanoscale

Strain engineering offers unique control to manipulate the electronic band structure of two-dimensional materials (2DMs) resulting in an effective and continuous tuning of the physical properties. Ad-hoc straining 2D materials has demonstrated novel devices including efficient photodetectors at telecommunication frequencies, enhanced-mobility transistors, and on-chip single photon source, for example. However, in order to gain insights into the underlying mechanism required to enhance the performance of the next-generation devices with strain(op)tronics, it is imperative to understand the nano- and microscopic properties as a function of a strong non-homogeneous strain. Here, we study the strain-induced variation of local conductivity of a few-layer transition-metal-dichalcogenide using a conductive atomic force microscopy. We report a novel strain characterization technique by capturing the electrical conductivity variations induced by local strain originating from surface topography at the nanoscale, which allows overcoming limitations of existing optical spectroscopy techniques. We show that the conductivity variations parallel the strain deviations across the geometry predicted by molecular dynamics simulation. These results substantiate a variation of the effective mass and surface charge density by .026 me/% and .03e/% of uniaxial strain, respectively. Furthermore, we show and quantify how a gradual reduction of the conduction band minima as a function of tensile strain explains the observed reduced effective Schottky barrier height. Such spatially-textured electronic behavior via surface topography induced strain variations in atomistic-layered materials at the nanoscale opens up new opportunities to control fundamental material properties and offers a myriad of design and functional device possibilities for electronics, nanophotonics, flextronics, or smart cloths.

cond-mat.mes-hall↗

arXiv2020

Extra Loss-free Non-Hermitian Engineered Single Mode Laser Systems

In a laser system non-Hermitian methods such as Parity-Time (PT) Symmetry and Supersymmetry (SUSY) have shown and demonstrated the ability to suppress unwanted lasing modes and, thus, achieved single mode lasing operation through the addition of lossy passive elements. While these approaches enable laser engineering versatility, they rely on the drawback of adding optical losses to a system tasked to produce single mode gain. Unlike PT and SUSY lasers, here we show an extra loss-free non-Hermitian laser engineering approach to realize single mode lasing operation for the first time. By selectively enhancing the fundamental modes quality factor, we obtain single mode operation with higher output power per cavity since all cavities in this system contribute to the laser output, in contrast to other non-Hermitian approaches. Furthermore, we show that this approach interestingly allows reducing the number of to-be-designed cavities in super-partner array as compared with, for example, the SUSY approach, thus leading to reduced design complexity upon coupled cavity scale up of laser arrays. In summary, the ability to engineer coupled laser systems where each laser cavity contributes to coherent light amplification opens up a new degree of laser-design freedom leading to increased device performance and simultaneous reduced design and fabrication complexity.

physics.optics↗

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

Low Dimensional Material based Electro-Optic Phase Modulation Performance Analysis

Electro-optic modulators are utilized ubiquitously ranging from applications in data communication to photonic neural networks. While tremendous progress has been made over the years, efficient phase-shifting modulators are challenged with fundamental tradeoffs, such as voltage-length, index change-losses or energy-bandwidth, and no single solution available checks all boxes. While voltage-driven phase modulators, such as based on lithium niobate, offer low loss and high speed operation, their footprint of 10's of cm-scale is prohibitively large, especially for density-critical applications, for example in photonic neural networks. Ignoring modulators for quantum applications, where loss is critical, here we distinguish between current versus voltage-driven modulators. We focus on the former, since current-based schemes of emerging thin electro-optical materials have shown unity-strong index modulation suitable for heterogeneous integration into foundry waveguides. Here, we provide an in-depth ab-initio analysis of obtainable modulator performance based on heterogeneously integrating low-dimensional materials, i.e. graphene, thin films of indium tin oxide, and transition metal dichalcogenide monolayers into a plurality of optical waveguide designs atop silicon photonics. Using the fundamental modulator tradeoff of energy-bandwidth-product as a design-quality quantifier, we show that a small modal cross section, such as given by plasmonic modes, enables high-performance operation, physically realized by arguments on charge-distribution and low electrical resistance. An in-depth design understanding of phase-modulator performance, beyond doped-junctions in silicon, offers opportunities for micrometer-compact yet energy-bandwidth-ratio constrained modulators with timely opportunities to hardware-accelerate applications beyond data communication towards photonic machine intelligence.

physics.app-ph↗

arXiv2020

Hexagonal Transverse Coupled Cavity VCSEL Redefining the High-Speed Lasers

The vertical-cavity surface-emitting lasers (VCSELs) have emerged as a vital approach for realizing energy efficient, high speed optical interconnects in the data center and supercomputers. As of today, VCSEL is the most suitable for mass production in terms of cost-effectiveness and reliability. However, there are still key challenges for higher speed modulation above 40 GHz. Here, a hexagonal transverse coupled cavity VCSEL adiabatically coupled through the center cavity is proposed. A 3-dB roll-off modulation bandwidth of 45 GHz is demonstrated, which is five times greater than a conventional VCSEL fabricated on the same epi-wafer structure. While a parity time (PT) symmetry approaches add loss to engineer the topological state of the laser system, here, a radical paradigm shift with gain introduces symmetry breaking. This idea, then enables a single mode operation with a side-mode suppression-ratio (SMSR) of > 30 decibels and signal-to-noise ratio (SNR) of > 45 decibels. The energy distribution inside the coupled cavity system is also redistributed to provide a coherent gain in a spatially separated system. Consequently, throughput power is three times higher than that of the conventional VCSEL.

physics.optics↗

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

Roadmap for Gain-Bandwidth-Product Enhanced Photodetectors

Photodetectors are key optoelectronic building blocks performing the essential optical-to-electrical signal conversion, and unlike solar cells, operate at a specific wavelength and at high signal or sensory speeds. Towards achieving high detector performance, device physics, however, places a fundamental limit of the achievable detector sensitivity, such as responsivity and gain, when simultaneously aimed to increasing the detectors temporal response, speed, known as the gain-bandwidth product (GBP). While detectors GBP has been increasing in recent years, the average GBP is still relatively modest (~10^6-10^7 Hz-A/W). Here we discuss photodetector performance limits and opportunities based on arguments from scaling length theory relating photocarrier channel length, mobility, electrical resistance with optical waveguide mode constrains. We show that short-channel detectors are synergistic with slot-waveguide approaches, and when combined, offer a high-degree of detector design synergy especially for the class of nanometer-thin materials. Indeed, we find that two dimensional material-based detectors are not limited by their low mobility and can, in principle, allow for 100 GHz fast response rates. However, contact resistance is still a challenge for such thin materials, a research topic that is still not addressed yet. An interim solution is to utilize heterojunction approaches for functionality separation. Nonetheless, atomistically- and nanometer-thin materials used in such next-generation scaling length theory based detectors also demand high material quality and monolithic integration strategies into photonic circuits including foundry-near processes. As it stands, this letter aims to guide the community if achieving the next generation photodetectors aiming for a performance target of GBP = 10^12 Hz-A/W.

physics.app-ph↗

arXiv2020

Silicon nitride grating based planar spectral splitting concentrator for NIR light Harvesting

We design a multi-layered solar spectral splitting planar concentrator for near infrared (NIR) light energy harvesting application. Each layer includes a silicon nitride based subwavelength diffraction grating on top of a glass substrate that is optimized to diffract the incoming solar radiation in a specific band from a broad spectral band (700-1400 nm in the NIR region) into guided modes propagating inside the glass substrate. The steep diffraction angle due to subwavelength grating results in concentrated light at the edge of each layer where it is then converted to electricity using a photovoltaic cell. The spectral splitting planar concentrator shows an overall NIR guiding efficiency of ~18%, and power conversion efficiency of ~11%. The design can be potentially used for building integrated photovoltaics application.

physics.optics↗

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↗

arXiv2020

A Design Methodology for Post-Moore's Law Accelerators: The Case of a Photonic Neuromorphic Processor

Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore's Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain-inspired computing has re-emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design-flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high-level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.

cs.ET↗