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Research Square 2020年

作者： Liao, Kun Chen, Ye Yu, Zhongcheng Hu, Xiaoyong Wang, Xingyuan Lu, Cuicui Lin, Hongtao Du, Qingyang Hu, Juejun Gong, Qihuang State Key Laboratory for Mesoscopic Physics & Department of Physics Collaborative Innovation Center of Quantum Matter Beijing Academy of Quantum Information Sciences Nano-optoelectronics Frontier Center of Ministry of Education Peking University Beijing100871 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China College of Mathematics and Physics Beijing University of Chemical Technology Beijing100029 China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-energy-consumption computing. Existing computing instruments are predominantly electronic processors, which use electrons as information carriers and possess von Neumann architecture featured by physical separation of storage and processing. The scaling of computing speed is limited not only by data transfer between memory and processing units, but also by RC delay associated with integrated circuits. Moreover, excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling. Using photons as information carriers is a promising alternative. Owing to weak third-order optical nonlinearity of conventional materials, building integrated photonic computing chips has been a challenge. Here, we report a strategy to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks, leveraging entirely linear optical interactions. The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed "weight modulators" to enable complete phase and amplitude control in each waveguide branch. The generic device concept can be used for equation solving, multifunctional logic operation, Fourier transformation, series expanding and encoding, as well as many other mathematical operations. Multiple computing functions including transcendental equation solvers, multifarious logic gate operators, and half-adders were experimentally demonstrated to validate the all-optical computing performances. The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit. Our approach can be further expanded to fulfill other complex computing tasks based on non-von Neumann architectures and thus

关键词： Data transfer

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Self-Calibrated Microring Weight Function for Neuromorphic optical computing

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第2期43卷 602-610页

作者： Echeverria, Jose Garcia Musat, Daniel Mahsafar, Ataollah Mojaver, Kaveh R. Rolston, David Cowan, Glenn Liboiron-Ladouceur, Odile McGill Univ Dept Elect & Comp Engn Montreal PQ H3A0E9 Canada Axonal Networks Montreal PQ H9W 3R4 Canada

This paper presents a microring resonator-based weight function for neuromorphic photonic applications achieving a record-high precision of 11.3 bits and accuracy of 9.3 bits for 2 Gbps input optical signals. The system employs an all-analog self-referenced proportional-integral-derivative (PID) controller to perform real-time temperature stabilization within a range of up to 60 degrees C. A self-calibrated weight function is demonstrated for a range of 6 degrees C with a single initial calibration and minimal accuracy and precision degradation. By monitoring the through and drop ports of the microring with variable gain transimpedance amplifiers, accurate and precise weight adjustment is achieved, ensuring optimal performance and reliability. These findings underscore the system's robustness to dynamic thermal environments, highlighting the potential for high-speed reconfigurable analog photonic networks.

关键词： optical waveguides Circuits Photonics optical feedback Thermal stability Monitoring Integrated optics neural networks optical computing optical ring resonators PID control silicon photonics

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Accelerating Convolutional Processing by Harnessing Channel Shifts in Arrayed Waveguide Gratings

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LASER & PHOTONICS REVIEWS 2025年第1期19卷

作者： Yi, Dan Zhao, Caiyue Zhang, Zunyue Xu, Hongnan Tsang, Hon Ki Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

Convolutional neural networks are a powerful category of artificial neural networks that can extract features from raw data to provide greatly reduced parametric complexity and enhance pattern recognition and the accuracy of prediction. optical neural networks offer the promise of dramatically accelerating computing speed while maintaining low power consumption even when using high-speed data streams running at hundreds of gigabit/s. Here, we propose an optical convolutional processor (CP) that leverages the spectral response of an arrayed waveguide grating (AWG) to enhance convolution speed by eliminating the need for repetitive element-wise multiplication. Our design features a balanced AWG configuration, enabling both positive and negative weightings essential for convolutional kernels. A proof-of-concept demonstration of an 8-bit resolution processor is experimentally implemented using a pair of AWGs with a broadband Mach-Zehnder interferometer (MZI) designed to achieve uniform weighting across the whole spectrum. Experimental results demonstrate the CP's effectiveness in edge detection and achieved 96% accuracy in a convolutional neural network for MNIST recognition. This approach can be extended to other common operations, such as pooling and deconvolution in Generative Adversarial networks. It is also scalable to more complex networks, making it suitable for applications like autonomous vehicles and real-time video recognition. A novel convolutional processor is proposed using the shifted spectral response of a pair of arrayed waveguide gratings (AWGs) to mimic the kernel shifts during image convolution. This inherent mixing of inputs in the AWG's spectral response eliminates the need for repetitive element-wise computations while enabling the simultaneous generation of convolved output maps. image

关键词： array waveguide grating convolutional processor deep learning image processing optical computing

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Integrated Photonic Convolution Accelerator Empowered by Thin-Film Lithium Niobate Modulators

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IEEE PHOTONICS TECHNOLOGY LETTERS 2025年第7期37卷 385-388页

作者： Zhou, Haojun Wu, Bo Zhang, Shiji Xu, Mengyue Wang, Jingyi Zhou, Hailong Cai, Xinlun Dong, Jianji Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol State Key Lab Optoelect Mat & Technol Guangzhou 510000 Peoples R China

The current optical convolution architectures are facing challenges related to limited scalability, excessive data redundancy and restricted processing bandwidth. In this work, we introduce an integrated photonic convolution accelerator (IPCA) empowered by high-speed thin-film lithium niobate (LN) modulators. Consequently, data replication redundancy is free and Fourier transform is avoided, which paves the way for highly efficient convolution scaling. We implement convolution in an optical frequency spacing of 8 GHz with a power consumption of only 52.9 mW. optical neural networks of different parameter sizes are constructed across various complex tasks. Given its advantages to address energy consumption and computing power challenges inherent to current AI advancements, our method heralds a pivotal shift in upcoming optical computing hardware architectures.

关键词： Convolution optical device fabrication optical modulation optical computing Training Frequency modulation Kernel High-speed optical techniques Frequency-domain analysis Radio frequency optical convolutional neural networks thin-film lithium niobate integrated photonics

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Inverse-designed integrated all-optical nonlinear activators for optical computing

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OPTICS EXPRESS 2024年第19期32卷 34001-34014页

作者： Yang, Zhan He, Jiajing Yan, Zhouyuan Hu, Yibiao Li, Xiaobo Dong, Ningning Wang, Jun Chinese Acad Sci Shanghai Inst Opt & Fine Mech Aerosp Laser Technol & Syst Dept CAS Key Lab Mat High Power Laser Shanghai 201800 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China CAS Ctr Excellence Ultraintense Laser Sci CEULS Shanghai 201800 Peoples R China

optical neural networks (ONNs) have been considered as an alternative solution to overcome the arithmetic and energy bottlenecks of electronic neural networks. However, the widespread implementation of ONNs is hindered by their lack of optical nonlinearity. In this work, three ultra-compact all-optical nonlinear activators are inverse-designed by combining the adjoint method and Kerr nonlinearity. The nonlinear response is mainly generated by the Kerr and the thermo-optic (TO) effect associated with the nonlinear refractive index. Transmission- as-computation and structure-as-function are realized, with a minimum activation threshold of 2.34 mW. In addition, we validated the feasibility and capability of the proposed method against benchmark machine learning tasks, in which the addition of nonlinear activation functions significantly improved the expressive power of the ONN, increasing the testing accuracy obtained from the Modified National Institute of Standards and Technology (MNIST) task from 88.15% to 93.25%. The proposed ONN framework with our nonlinear activators exhibited good robustness against phase errors in the network topology. We believe that this study contributes to the future development of large-scale chip-level ONNs. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Information processing Machine learning neural networks optical computing optical neural systems Refractive index

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Implementation of energy-efficient convolutional neural networks based on kernel-pruned silicon photonics

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OPTICS EXPRESS 2023年第16期31卷 25865-25880页

作者： Huang, Duan Xiong, Yanting Xing, Zhuangzhuang Zhang, Qi Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Silicon-based optical neural networks offer the prospect of high-performance computing on integrated photonic circuits. However, the scalability of on-chip optical depth networks is restricted by the limited energy and space resources. Here, we present a silicon-based photonic convolutional neural network (PCNN) combined with the kernel pruning, in which the optical convolutional computing core of PCNN is a tunable micro-ring weight bank. Our numerical simulation demonstrates the effect of weight mapping accuracy on PCNN performance and we find that the performance of PCNN decreases significantly when the weight mapping accuracy is less than 4.3 bits. Additionally, the experimental demonstration shows that the accuracy of the PCNN on the MNIST dataset has a slight loss compared to the original CNN when 93.75 % of the convolutional kernels are pruned. By making use of kernel pruning, the energy saved by a convolutional kernel removal is about 202.3 mW, and the overall energy saved has a linear relationship with the number of kernels removed. The methodology is scalable and provides a feasible solution for implementing faster and more energy-efficient large-scale optical convolutional neural networks on photonic integrated circuits.

关键词： neural networks Numerical simulation optical components optical networks optical neural systems Silicon photonics

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neural networks with quantum states of light

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PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2024年第2287期382卷 20230346页

作者： Labay-Mora, Adria Garcia-Beni, Jorge Giorgi, Gian Luca Soriano, Miguel C. Zambrini, Roberta Campus Univ Illes Balears Inst Cross Disciplinary Phys & Complex Syst IFISC UIB CSIC E-07122 Palma De Mallorca Spain

Quantum optical networks are instrumental in addressing the fundamental questions and enable applications ranging from communication to computation and, more recently, machine learning (ML). In particular, photonic artificial neural networks (ANNs) offer the opportunity to exploit the advantages of both classical and quantum optics. Photonic neuro-inspired computation and ML have been successfully demonstrated in classical settings, while quantum optical networks have triggered breakthrough applications such as teleportation, quantum key distribution and quantum computing. We present a perspective on the state of the art in quantum optical ML and the potential advantages of ANNs in circuit designs and beyond, in more general, analogue settings characterized by recurrent and coherent complex interactions. We consider two analogue neuro-inspired applications, namely quantum reservoir computing and quantum associative memories, and discuss the enhanced capabilities offered by quantum substrates, highlighting the specific role of light squeezing in this *** article is part of the theme issue 'The quantum theory of light'.

关键词： quantum machine learning quantum optics squeezing

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Integration of Programmable Diffraction with Digital neural networks

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ACS PHOTONICS 2024年第8期11卷 2906-2922页

作者： Rahman, Md Sadman Sakib Ozcan, Aydogan Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA Univ Calif Los Angeles Bioengn Dept Los Angeles CA 90095 USA Univ Calif Los Angeles Calif NanoSyst Inst CNSI Los Angeles CA 90095 USA

optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades. Earlier generations of diffractive optical processors were, in general, designed to deliver information to an independent system that was separately optimized, primarily driven by human vision or perception. With the recent advances in deep learning and digital neural networks, there have been efforts to establish diffractive processors that are jointly optimized with digital neural networks serving as their back-end. These jointly optimized hybrid (optical + digital) processors establish a new "diffractive language" between input electromagnetic waves that carry analog information and neural networks that process the digitized information at the back-end, providing the best of both worlds. Such hybrid designs can process spatially and temporally coherent, partially coherent, or incoherent input waves, providing universal coverage for any spatially varying set of point spread functions that can be optimized for a given task, executed in collaboration with digital neural networks. In this Perspective, we highlight the utility of this exciting collaboration between engineered and programmed diffraction and digital neural networks for a diverse range of applications. We survey some of the major innovations enabled by the push-pull relationship between analogue wave processing and digital neural networks, also covering the significant benefits that could be reaped through the synergy between these two complementary paradigms.

关键词： diffractive computing programmed diffraction diffractive networks hybrid information processing deep optics

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All-optical DCT Encoding and Information Compression Based on Diffraction neural Network

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ACS PHOTONICS 2025年第2期12卷 1196-1211页

作者： Ren, He Feng, Yuxiang Zhou, Shuai Wang, Di Yang, Xu Chen, Shouqian Harbin Inst Technol Natl Key Lab Laser Spatial Informat Harbin 150001 Peoples R China Beijing Aerosp Inst Metrol & Measurement Technol Beijing 100076 Peoples R China

As information technology advances and data volumes grow rapidly, there is an increasing requirement for information security and throughput in image transmission systems. Diffractive neural networks (DNNs), a novel all-optical information processing paradigm, offer several advantages including high-speed processing, low energy consumption, and high spatial utilization. These networks also leverage the powerful reverse-design capabilities of deep learning methods, enabling the efficient implementation of various image information encoding techniques. The discrete cosine transform (DCT), a well-established technology widely used in image encoding, shares features with DNNs, such as linear operations, spatial-frequency domain conversions, and high parallelism. This research focuses on building an all-optical DCT processor based on the DNN architecture (DCT-DNN). Testing revealed that this processor performed DCT operations on random matrices and achieved DCT-based compression on specific data sets. Additionally, the DCT with a block for large-sized images was validated. The DCT-DNN, with its high speed and low energy consumption, can be integrated with other complex optoelectronic computing systems to serve as a general computing device for computational acceleration. Furthermore, it can be combined with data transmission systems or directly integrated into image information collection systems to encode and transmit front-end collected information. This makes it a valuable tool for data processing, encryption, and transmission applications.

关键词： optical computing diffractive neural network metasurface image compression

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Multilayer optical neural network using saturable absorber for nonlinearity

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OPTICS COMMUNICATIONS 2025年 577卷

作者： Gupta, Kalpak Lee, Ye-Ryoung Cho, Ye-Chan Choi, Wonshik Ctr Mol Spect & Dynam Inst Basic Sci Seoul 02841 South Korea Korea Univ Dept Phys Seoul 02855 South Korea Konkuk Univ Dept Phys Educ Seoul 05029 South Korea

Over the past few years, neural networks (NNs) have become indispensable for a variety of applications. However, the increasing complexity and resource demands of traditional NNs have prompted the exploration of alternative platforms for neuromorphic computing. optical neural networks (ONNs), which leverage the properties of light for computation, offer a promising solution due to their advantages, including low power consumption and high speed. Here, we propose an all-optical ONN for image classification that utilizes various optical elements to perform key NN operations. The proposed network is based on the framework of reservoir computing and makes use of a scattering medium for linear mapping, a saturable absorber for nonlinear activation, and phase biasing for trainable classification layers. The incorporation of multiple layers, a common practice in NNs to enhance performance, is also explored. The feasibility of the proposed ONN is demonstrated through simulations on various standard image datasets, showing that the incorporation of nonlinearity and multiple learning layers significantly improves image classification accuracy, by up to 30%.

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