Deep learning with coherent nanophotonic circuits
01 Jul 2017-Vol. 11, Iss: 7, pp 441-446
TL;DR: A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.
Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.
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TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Abstract: Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
1,504 citations
TL;DR: Monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels are demonstrated.
Abstract: Electro-optic modulators translate high-speed electronic signals into the optical domain and are critical components in modern telecommunication networks1,2 and microwave-photonic systems3,4. They are also expected to be building blocks for emerging applications such as quantum photonics5,6 and non-reciprocal optics7,8. All of these applications require chip-scale electro-optic modulators that operate at voltages compatible with complementary metal–oxide–semiconductor (CMOS) technology, have ultra-high electro-optic bandwidths and feature very low optical losses. Integrated modulator platforms based on materials such as silicon, indium phosphide or polymers have not yet been able to meet these requirements simultaneously because of the intrinsic limitations of the materials used. On the other hand, lithium niobate electro-optic modulators, the workhorse of the optoelectronic industry for decades9, have been challenging to integrate on-chip because of difficulties in microstructuring lithium niobate. The current generation of lithium niobate modulators are bulky, expensive, limited in bandwidth and require high drive voltages, and thus are unable to reach the full potential of the material. Here we overcome these limitations and demonstrate monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels. We achieve this by engineering the microwave and photonic circuits to achieve high electro-optical efficiencies, ultra-low optical losses and group-velocity matching simultaneously. Our scalable modulator devices could provide cost-effective, low-power and ultra-high-speed solutions for next-generation optical communication networks and microwave photonic systems. Furthermore, our approach could lead to large-scale ultra-low-loss photonic circuits that are reconfigurable on a picosecond timescale, enabling a wide range of quantum and classical applications5,10,11 including feed-forward photonic quantum computation. Chip-scale lithium niobate electro-optic modulators that rapidly convert electrical to optical signals and use CMOS-compatible voltages could prove useful in optical communication networks, microwave photonic systems and photonic computation.
1,358 citations
TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Abstract: Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D2NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We created 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D2NNs.
1,145 citations
TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
Abstract: Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
862 citations
TL;DR: A tandem neural network architecture is demonstrated that tolerates inconsistent training instances in inverse design of nanophotonic devices and provides a way to train large neural networks for the inverseDesign of complex photonic structures.
Abstract: Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of nonuniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain nonunique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that require large training data sets.
619 citations
References
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20 Aug 2015
TL;DR: A new architecture and a novel self-adjustment approach are proposed that automatically compensate for imperfect fabricated split ratios anywhere from 85∶15 to 15∶85, and one universal field-programmable linear array optical element could be mass fabricated, with broad process tolerances, and then configured automatically for a wide range of complex and precise linear optical functions.
Abstract: Many advanced optical functions, including spatial mode converters, linear optics quantum computing gates, and arbitrary linear optical processors for communications and other applications could be implemented using meshes of Mach–Zehnder interferometers in technologies such as silicon photonics, but performance is limited by beam splitters that deviate from the ideal 50∶50 split. We propose a new architecture and a novel self-adjustment approach that automatically compensate for imperfect fabricated split ratios anywhere from 85∶15 to 15∶85. The entire mesh can be both optimized and programmed after initial fabrication, with progressive algorithms, without calculations or calibration, and even using only sources and detectors external to the mesh. Hence, one universal field-programmable linear array optical element could be mass fabricated, with broad process tolerances, and then configured automatically for a wide range of complex and precise linear optical functions.
272 citations
"Deep learning with coherent nanopho..." refers background in this paper
...It was theoretically shown that any unitary transformations U, V∗ can be implemented with optical beamsplitters and phase shifters [18, 19]....
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TL;DR: In this paper, a low-loss silicon waveguide fabricated without any silicon etching is presented, which produces ultra-smooth sidewalls with width variations of 0.3 nm.
Abstract: We demonstrate low loss silicon waveguides fabricated without any silicon etching. We define the waveguides by selective oxidation which produces ultra-smooth sidewalls with width variations of 0.3 nm. The waveguides have a propagation loss of 0.3 dB/cm at 1.55 μm. The waveguide geometry enables low bending loss of approximately 0.007 dB/bend for a 90° bend with a 50 μm bending radius.
262 citations
TL;DR: The field is reaching a critical juncture at which there is a shift from studying single devices to studying an interconnected network of lasers, and the recent research in the information processing abilities of such lasers are reviewed, dubbed “photonic neurons,” “laser neurons” or “optical neurons.”
Abstract: Recently, there has been tremendous interest in excitable optoelectronic devices and in particular excitable semiconductor lasers that could potentially enable unconventional processing approaches beyond conventional binary-logic-based approaches. In parallel, there has been renewed investigation of non-von Neumann architectures driven in part by incipient limitations in aspects of Moore’s law. These neuromorphic architectures attempt to decentralize processing by interweaving interconnection with computing while simultaneously incorporating time-resolved dynamics, loosely classified as spiking (a.k.a. excitability). The rapid and efficient advances in CMOS-compatible photonic interconnect technologies have led to opportunities in optics and photonics for unconventional circuits and systems. Effort in the budding research field of photonic spike processing aims to synergistically integrate the underlying physics of photonics with bio-inspired processing. Lasers operating in the excitable regime are dynamically analogous with the spiking dynamics observed in neuron biophysics but roughly 8 orders of magnitude faster. The field is reaching a critical juncture at which there is a shift from studying single devices to studying an interconnected network of lasers. In this paper, we review the recent research in the information processing abilities of such lasers, dubbed “photonic neurons,” “laser neurons,” or “optical neurons.” An integrated network of such lasers on a chip could potentially grant the capacity for complex, ultrafast categorization and decision making to provide a range of computing and signal processing applications, such as sensing and manipulating the radio frequency spectrum and for hypersonic aircraft control.
213 citations
TL;DR: In the inaugural issue of this Journal, Indiveri et al. (2011) review the current state of the art in CMOS-based neuromorphic neuron circuit designs that have evolved over the past two decades and delineates and compares the latest SiN design techniques as applied to varying types of spiking neuron models ranging from realistic conductancebased Hodgkin–Huxley models to simple yet versatile integrate-and-fire models.
Abstract: Neuromorphic silicoN NeuroNs: state of the art Complementary metal-oxide-semiconductor (CMOS) transistors are commonly used in very-large-scale-integration (VLSI) digital circuits as a basic binary switch that turns on or off as the transistor gate voltage crosses some threshold. Carver Mead first noted that CMOS transistor circuits operating below this threshold in current mode have strikingly similar sigmoidal current– voltage relationships as do neuronal ion channels and consume little power; hence they are ideal analogs of neuronal function (Mead, 1989). This unique device physics led to the advent of “neuromorphic” silicon neurons (SiNs) which allow neuronal spiking dynamics to be directly emulated on analog VLSI chips without the need for digital software simulation (Mahowald and Douglas, 1991). In the inaugural issue of this Journal, Indiveri et al. (2011) review the current state of the art in CMOS-based neuromorphic neuron circuit designs that have evolved over the past two decades. The comprehensive appraisal delineates and compares the latest SiN design techniques as applied to varying types of spiking neuron models ranging from realistic conductancebased Hodgkin–Huxley models to simple yet versatile integrate-and-fire models. The timely and much needed compendium is a tour de force that will certainly provide a valuable guidepost for future SiN designs and applications.
201 citations
TL;DR: In this paper, a resistive heater optimized for efficient and low-loss optical phase modulation in a silicon-on-insulator (SOI) waveguide was designed and a 61.6 μm long phase shifter was fabricated.
Abstract: We design a resistive heater optimized for efficient and low-loss optical phase modulation in a silicon-on-insulator (SOI) waveguide and characterize the fabricated devices. Modulation is achieved by flowing current perpendicular to a new ridge waveguide geometry. The resistance profile is engineered using different dopant concentrations to obtain localized heat generation and maximize the overlap between the optical mode and the high temperature regions of the structure, while simultaneously minimizing optical loss due to free-carrier absorption. A 61.6 μm long phase shifter was fabricated in a CMOS process with oxide cladding and two metal layers. The device features a phase-shifting efficiency of 24.77 ± 0.43 mW/π and a −3 dB modulation bandwidth of 130.0 ± 5.59 kHz; the insertion loss measured for 21 devices across an 8-inch wafer was only 0.23 ± 0.13 dB. Considering the prospect of densely integrated photonic circuits, we also quantify the separation necessary to isolate thermo-optic devices in the standard 220 nm SOI platform.
201 citations