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Chip-Based High-Dimensional optical neural network

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Nano-Micro Letters 2022年第12期14卷 570-578页

作者： Xinyu Wang Peng Xie Bohan Chen Xingcai Zhang School of Future Technology University of Chinese Academy of SciencesBeijing 100049China Department of Engineering Science University of OxfordParks RoadOxford OX13PJUK School of Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing *** neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big ***,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal *** frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the *** successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power *** parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional *** work provides a high-performance architecture for future parallel high-capacity optical analog computing.

关键词： Integrated optics optical neural network High-dimension Mach-Zehnder interferometer Nonlinear activation function Parallel high-capacity analog computing

All optical artificial synapses based on long-afterglow material for optical neural network

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Nano Research 2023年第7期16卷 10004-10010页

作者： Wenjie Lu Qizhen Chen Huaan Zeng Hui Wang Lujian Liu Tailiang Guo Huipeng Chen Rui Wang Institute of Optoelectronic Display National&Local United Engineering Lab of Flat Panel Display TechnologyFuzhou UniversityFuzhou 350002China Department of Neurosurgery Fujian Medical University Union HospitalFuzhou 350001China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350100China

Artificial neural network with broad application prospect has attracted particular attention due to the promise of solving the memory wall *** neural devices that mix light and electricity provide more degrees of freedom for the design of artificial neural network,but they still do not get rid of the shackles that the response signal needs circuit to *** exploration of all-optical neural devices(optical signal input and output)is expected to solve this ***,an all-optical synaptic device simply based on a long-afterglow material is *** optical properties of the all-optical synaptic device are similar to the responses in biological *** image displays and memory functions can be achieved by combining alloptical synaptic arrays with synaptic memory ***,the optical summation of all-optical synaptic array pixels can be completed by combining the focusing characteristics of convex lens,which realizes the photon transmission after preprocessing multiple input ***,the simple single-layer structure of all-optical synapses with polydimethylsiloxane(PDMS)as the carrier has high plasticity and is expected to achieve large-scale *** work enriches the diversity of artificial synapses and shows the huge development potential of photoelectric artificial neural networks.

关键词： synaptic plasticity all optical optical computation optical neural network

Computation at the speed of light:metamaterials for all-optical calculations and neural networks

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Advanced Photonics 2022年第6期4卷 23-43页

作者： Trevon Badloe Seokho Lee Junsuk Rho Pohang University of Science and Technology Department of Mechanical EngineeringPohangRepublic of Korea Pohang University of Science and Technology Department of Chemical EngineeringPohangRepublic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics PohangRepublic of Korea

The explosion in the amount of information that is being processed is prompting the need for new computing systems beyond existing electronic *** computing is emerging as an attractive alternative due to performing calculations at the speed of light,the change for massive parallelism,and also extremely low energy *** review the physical implementation of basic optical calculations,such as differentiation and integration,using metamaterials,and introduce the realization of all-optical artificial neural *** start with concise introductions of the mathematical principles behind such optical computation methods and present the advantages,current problems that need to be overcome,and the potential future directions in the *** expect that our review will be useful for both novice and experienced researchers in the field of all-optical computing platforms using metamaterials.

关键词： photonic computing all-optical calculation optical neural network programmable metasurface

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

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Advanced Photonics 2023年第1期5卷 27-49页

作者： Jingxi Li Tianyi Gan Bijie Bai Yi Luo Mona Jarrahi Aydogan Ozcan University of California Electrical and Computer Engineering DepartmentLos AngelesCaliforniaUnited States University of California Bioengineering DepartmentLos AngelesCaliforniaUnited States University of California California NanoSystems InstituteLos AngelesCaliforniaUnited States

Large-scale linear operations are the cornerstone for performing complex computational *** optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and ***,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband *** report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,*** broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥*** further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation *** parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

关键词： optical neural network deep learning diffractive optical network wavelength multiplexing optical computing

Structural plasticity-based hydrogel optical Willshaw model for one-shot on-the-fly edge learning

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InfoMat 2023年第4期5卷 48-59页

作者： Dingchen Wang Dingyao Liu Yinan Lin Anran Yuan Woyu Zhang Yaping Zhao Shaocong Wang Xi Chen Hegan Chen Yi Zhang Yang Jiang Shuhui Shi Kam Chi Loong Jia Chen Songrui Wei Qing Wang Hongyu Yu Renjing Xu Dashan Shang Han Zhang Shiming Zhang Zhongrui Wang Department of Electrical and Electronic Engineering The University of Hong KongHong Kongthe People's Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kongthe People's Republic of China School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaothe People's Republic of China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijingthe People's Republic of China Collaborative Innovation Center for Optoelectronic Science Technology International Collaborative Laboratory of 2D Materials for OptoelectronicsScience and Technology of Ministry of EducationInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhenthe People's Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhenthe People's Republic of China Microelectronics Thrust Function Hub of the Hong Kong University of Science and Technology(Guangzhou)Guagndongthe People's Republic of China

Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the *** such learning on digital hardware suffers from the well-known von-Neumann and scaling *** optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising ***,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge ***,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical *** first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and *** then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot *** HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.

关键词： associative memory hydrogel one-shot learning optical neural network structural plasticity Willshaw model

Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

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Light(Advanced Manufacturing) 2023年第3期4卷 206-221页

作者： Yuhang Li Yi Luo Deniz Mengu Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical *** QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image ***,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase *** QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light *** all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and *** presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

关键词： Quantitative phase imaging optical neural network Diffractive deep neural network Diffusive media All-optical computing

Towards an intelligent photonic system

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Science China(Information Sciences) 2020年第6期63卷 80-96页

作者： Weiwen ZOU Bowen MA Shaofu XU Xiuting ZOU Xingjun WANG State Key Laboratory of Advanced optical Communication Systems and networks Intelligent Microwave Lightwave Integration Innovation Center (iMLic) Department of Electronic EngineeringShanghai Jiao Tong University State Key Laboratory of Advanced optical Communication Systems and networks School of Electronics Engineering and Computer Science Peking University

The emerging intelligence technologies represented by deep learning have broadened their applications to various fields. Beyond the conventional electronics-based processing systems, the convergence of photonics and artificial intelligence(AI) technology enhances the performance and learning ability of AI. In this review, we propose the concept of an intelligent photonic system(IPS), illustrating it as a developing architecture with three different versions. For each version of IPS, we review several representative *** we discuss the challenges towards an IPS and provide some prospects for the future development.

关键词： intelligent photonic system deep learning artificial intelligence optical neural network neuromorphic photonics