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photonics approaches to the implementation of neuromorphic computing

PHYSICS-USPEKHI 2023年第12期66卷 1284-1297页

作者： Musorin, A., I Shorokhov, A. S. Chezhegov, A. A. Baluyan, T. G. Safronov, K. R. Chetvertukhin, A., v Grunin, A. A. Fedyanin, A. A. Lomonosov Moscow State Univ Fac Phys Leninskie Gory 1Str 2 Moscow 119991 Russia

Physical limitations on the operation speed of electronic devices has motivated the search for alternative ways to process information. The past few years have seen the development of neuromorphic photonicsia branch of photonics where the physics of optical and optoelectronic devices is combined with mathematical algorithms of artiecial neural networks. Such a symbiosis allows certain classes of computation problems, including some involving artiecial intelligence, to be solved with greater speed and higher energy efeciency than can be reached with electronic devices based on the von Neumann architecture. We review optical analog computing, photonic neural networks, and methods of matrix multiplication by optical means, and discuss the advantages and disadvantages of existing approaches.

关键词： neuromorphic photonics artificial intelligence machine learning reservoir computing matrix +/- vector multiplication photonic computing neural networks optical coprocessor photonic tensor computing optical Fourier transform integrated photonics Mach +/- Zehnder interferometer ring resonators waveguides

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Monolithically integrated optical computing component for photonic matrix multiplication based on electro-optically modulated vertical-cavity surface-emitting lasers 11

Monolithically integrated optical computing component for ph...

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Conference on Semiconductor Lasers and Laser Dynamics XI

作者： Ledentsov, N., Jr. Dijkstra, J. Makarov, Yu Shchukin, v. A. Chorchos, L. Kropp, J-R Pernice, W. H. P. Ledentsov, N. N. VI Syst GmbH Hardenbergstr 7 D-10623 Berlin Germany Heidelberg Univ Kirchhoff Inst Phys Grabengasse 1 D-69117 Heidelberg Germany

ISBN: (纸本)9781510673236;9781510673229

Leading-edge machine learning algorithms require large amounts of matrix multiplications, which absorb significant computational resources in modern digital electronic systems. Analogue optical computing systems consisting of light sources, modulators and receivers may perform these operations with much higher efficiency. We introduce an integrated device based on electro-optically modulated (EOM) vertical-cavity surface-emitting lasers (vCSELs) that can perform analog multiplication at >28 GHz and at <20 mW power consumption. Due to its monolithic integration, the EOM vCSEL may be used as a building block for integrated optical computing devices. Development of such devices can help create 3-dimensionally integrated computing and communication systems.

关键词： vertical cavity surface-emitting laser optical computing analogue computing photonic integrated circuits

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Quantum circuits with many photons on a programmable nanophotonic chip

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NATURE 2021年第7848期591卷 54-+页

作者： Arrazola, J. M. Bergholm, v Bradler, K. Bromley, T. R. Collins, M. J. Dhand, I Fumagalli, A. Gerrits, T. Goussev, A. Helt, L. G. Hundal, J. Isacsson, T. Israel, R. B. Izaac, J. Jahangiri, S. Janik, R. Killoran, N. Kumar, S. P. Lavoie, J. Lita, A. E. Mahler, D. H. Menotti, M. Morrison, B. Nam, S. W. Neuhaus, L. Qi, H. Y. Quesada, N. Repingon, A. Sabapathy, K. K. Schuld, M. Su, D. Swinarton, J. Szava, A. Tan, K. Tan, P. vaidya, v. D. vernon, Z. Zabaneh, Z. Zhang, Y. Xanadu Toronto ON Canada NIST Boulder CO USA

Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms(1,2). Present-day photonic quantum computers(3-7) have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra and graph similarity(8). These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing.

关键词： photonic devices Quantum information Quantum optics

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BraggNet: Complex photonic Integrated Circuit Reconstruction Using Deep Learning

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2021年第6期27卷 1页

作者： Cauchon, Jonathan vallee, Jean-Michel St-Yves, Jonathan Shi, Wei Univ Laval Ctr Opt Photon & Laser COPL Quebec City PQ G1V 0A6 Canada

We propose a deep learning model to reconstruct physical designs of complex coupled photonic systems, such as waveguide Bragg gratings, fromtheir spectral responses for inverse design and fabrication diagnosis. Traditional reconstructing algorithms demand considerable computing resources at every query. Conversely, machine learning algorithms usemost of the computing resources during the training process and provide effortless and orders-of-magnitude faster analysis in response to queries. This approach is demonstrated using silicon photonic grating-assisted, contra-directional couplers consisting of thousands of Bragg periods. The contra-directional couplers are modeled as coupled cavities, for which a transfer matrix model is used to generate a synthetic dataset comprising a strategic design parameter space. The free-form, architecture-independent model allows to include any geometries to the design parameter space. Upon proper training, themodel achieves 1.4% mean absolute percentage error on device reconstruction and thus proves suitable for inverse design applications. To further show its potential for assessment of fabricated devices, another dataset is generated to emulate the fabrication conditions of a nominal design hindered by fabrication imperfections. Themodel is shown to reconstruct devices from experimental measurements with greater than 600-fold improvement in speed compared to the classical layer-peeling algorithm. This proves promising for data-driven processes required by Industry 4.0.

关键词： Silicon photonics photonic integrated circuits bragg gratings machine learning artificial intelligence neural networks fabrication diagnosis

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Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization

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APPLIED PHYSICS REvIEWS 2020年第2期7卷 1-10页

作者： Kudyshev, Zhaxylyk A. Kildishev, Alexander v. Shalaev, vladimir M. Boltasseva, Alexandra Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA Purdue Univ Birck Nanotechnol Ctr W Lafayette IN 47906 USA Purdue Univ Ctr Sci Informat W Lafayette IN 47906 USA

Nanophotonic devices can provide solutions to challenges in energy conversion, information technologies, chemical or biological sensing, quantum computing, and secure communications. The realization of practical optical structures and devices is a complex problem due to the multitude of constraints on their optical performance, materials, scalability, and experimental tolerances, all of which are requirements implying large optimization spaces. However, despite the complexity of the process, to date, almost all nanophotonic structures are designed either intuitively or based on a priori selected topologies, and by adjusting a limited number of parameters. These intuition-based models are limited to ad hoc needs and have narrow applicability and predictive power, with the exhaustive parameter searches often performed manually. Since the comprehensive search in hyper-dimensional design space is highly resource-heavy, multi-objective optimization has so far been almost impossible. Humans' restrained capacity to think hyper-dimensionally also limits the perception of multivariate optimization models, and, therefore, advanced machinery is needed to manage the multi-domain, hyper-dimensional design parameter space. In this work, we merge the topology optimization method with deep learning algorithms, such as adversarial autoencoders, and show substantial improvement of the optimization process in terms of computational time (4900 times faster) and final devices efficiencies (similar to 98%) by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.

关键词： Deep learning

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Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices

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APPLIED SOFT computing 2018年 65卷 1-11页

作者： Ferreira, Adriano da Silva da Silva Santos, Carlos Henrique Goncalves, Marcos Sergio Hernandez Figueroa, Hugo Enrique Univ Estadual Campinas Sch Elect & Comp Engn Dept Commun BR-13083852 Campinas SP Brazil Sao Paulo Fed Inst Educ Sci & Technol Campus Itapetininga BR-18202000 Itapetininga Brazil Univ Estadual Campinas Sch Technol BR-13484332 Limeira Brazil

photonics has been widely explored in computing and communications, mainly to rationalize the relationship between device size minimization and data processing/transmission maximization. Generally driven by optimization and modeling techniques, the design of photonic devices is often performed by bio-inspired algorithms integrated to electromagnetic solvers, which have achieved advances but is still time-consuming. As an alternative to a costly finite element method (FEM) solver, a multilayer perceptron (MLP) neural network is proposed for computing power coupling efficiency of photonic couplers, originally designed through an integrated evolutionary strategy (ES) and FEM routine. We address the ES-FEM design of two efficient couplers, present the MLP implementation and the MLP training and testing over the routine generated datasets, and measure MLP and FEM runtime. MLP suitably predicted the power coupling efficiency of a variety of unknown couplers on tests. The measured runtime showed MLP is similar to 10(5) faster than FEM. In conclusion, MLP is a potential tool to be integrated to ES on the design of such photonic couplers. (c) 2017 Elsevier B.v. All rights reserved.

关键词： photonic coupler Evolutionary algorithm Artificial neural network Device optimization

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Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization

arXiv

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arXiv 2019年

作者： Kudyshev, Zhaxylyk A. Kildishev, Alexander v. Shalaev, vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47906 United States Center for Science of Information Purdue University West LafayetteIN47906 United States

With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion, information technologies, chemical/biological sensing, space exploration, quantum computing, and secure communication. Addressing grand challenges poses inherently complex, multi-disciplinary problems with a manifold of stringent constraints in conjunction with the required system's performance. Conventional optimization techniques have long been utilized as powerful tools to address multiconstrained design tasks. One example is so-called topology optimization that has emerged as a highly successful architect for the advanced design of non-intuitive photonic structures. Despite many advantages, this technique requires substantial computational resources and thus has very limited applicability to highly constrained optimization problems within high-dimensions parametric space. In our approach, we merge the topology optimization method with machine learning algorithms such as adversarial autoencoders and show substantial improvement of the optimization process by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Space research

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photonic Reservoir Computer With Output Feedback for Chaotic Time Series Prediction

Photonic Reservoir Computer With Output Feedback for Chaotic...

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International Joint Conference on Neural Networks

作者： Piotr Antonik Michiel Hermans Marc Haelterman Serge Massar Laboratoire d'Information Quantique Université libre de Bruxelles Avenue F. D. Roosevelt 50 CP 224 Brussels Belgium Service OPERA-Photonique Université libre de Bruxelles Avenue F. D. Roosevelt 50 CP 194/5 Brussels Belgium

ISBN: (纸本)9781509061839

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. It can be easily implemented in hardware. The performance of these analogue devices matches digital algorithms on a series of benchmark tasks. Their capacities could be extended by feeding the output signal back into the reservoir, which would allow them to be applied to various signal generation tasks. In practice, this requires a high-speed readout layer for real-time output computation. Here we achieve this by means of an FPGA chip, and demonstrate the first photonic reservoir computer with output feedback. We test our setup on the Mackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement. Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications for photonic reservoir computing.

关键词： Reservoirs Computers Field programmable gate arrays Output feedback Time series analysis Training photonics

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Designing supercontinuum spectra using Grid technology

Designing supercontinuum spectra using Grid technology

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2nd Workshop on Specialty Optical Fibers and Their Applications

作者： Ferrando, A. Milian, C. Gonzalez, N. Molto, G. Loza, P. Arevalillo-Herraez, M. Zacares, M. Torres-Gomez, I. Hernandez, v. Univ Valencia Dept Opt Dr Moliner 50 E-46100 Burjassot Valencia Spain

ISBN: (纸本)9780819483607

We present a new computational scheme to design supercontinuum spectra "a la carte" by means of Genetic algorithms. Due to the potentially large amount of computations required by this strategy, the deployment of these heuristic algorithms is performed using distributed computing in the form of a Grid platform. The optimization procedure is automated within the Grid platform and permits escalation to large computational Grids. Some examples of designed supercontinua are given and potential applications for the design of future photonic devices are briefly described.

关键词： Supercontinuum optimization genetic-algorithms Grid technology

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Hybrid intelligent approach for modeling and optimization of semiconductor devices and nanostructures

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Computational Materials Science 2009年第1期45卷 41-51页

作者： Li, Yiming Department of Communication Engineering National Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 300 Taiwan

In this work, we present a hybrid intelligent approach for parameter extraction and design optimization of semiconductor nanoscale devices and nanostructures. Based on evolutionary algorithms, numerical methods, neural network scheme and parallel computing technique, the optimization methodology is developed and successfully implemented. In the hybrid approach, an evolutionary algorithm, such as genetic algorithm or particle swarm optimization, firstly searches the entire problem space to get a set of roughly estimated solutions. The numerical method, such as Levenberg-Marquardt method, then performs a local optima search and sets the local optima as the suggested values for the genetic algorithm to perform further optimizations. Meanwhile, the neural network is applied to investigate the influence of parameters on the optimized functions which thus guides the evolutionary direction of genetic algorithm. For solving real world problems, all functional blocks are performed under a PC-based Linux cluster system with message-passing interface libraries. This hybrid intelligent approach has experimentally been implemented and validated for different applications in semiconductor nanodevices and nanostructures. For semiconductor nanodevice parameter extraction, this approach shows its capability to automatically extract a set of global parameters among sixteen 90 nm complementary metal oxide semiconductor (CMOS) devices. Compared with the measured current-voltage (I-v) curves of fabricated CMOS samples, the optimized I-v results are within 3% of accuracy. The computational examinations including sensitivity, convergence property, and parallelization are discussed. For parameter extraction of organic light emitting diode (OLED), the approach also achieves good accuracy for red, green, blue OLEDs. For the third and fourth applications, optimal structure design of silicon photonic taper waveguide and photonic crystal are further advanced by integrating a simulation-based te

关键词： Numerical methods

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