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Silicon photonic convolution operator exploiting on-chip nonlinear activation function

OPTICS LETTERS 2025年第2期50卷 582-585页

作者： Tao, Zilong You, Jie Hao, Ouyang Yan, Qiuquan Du, Shiyin Zhang, Jun Jiang, Tian Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Acad Mil Sci PLA China Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Sci & Technol Coll Sci Changsha 410073 Peoples R China

Nonlinear activation functions (NAFs) are essential in artificial neural networks, enhancing learning capabilities by capturing complex input-output relationships. However, most NAF implementations rely on additional optoelectronic devices or digital computers, reducing the benefits of optical computing. To address this, we propose what we believe to be the first implementation of a nonlinear modulation process using an electro-optic IQ modulator on a silicon photonic convolution operator chip as a novel NAF. We validated this operator by constructing a convolutional neural network for radio machine learning classification, achieving 92.5% accuracy-an improvement of 27% over the case without a NAF. Compared with optoelectronic systems that rely on separate components, this fully integrated silicon photonic chip allows the NAF to execute nearly synchronously with the convolution operation, significantly lowering latency and reducing the complexity of the peripheral control system. This work paves the way for a large-scale on-chip optical neural network computation. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Machine learning neural networks optical computing optical neural systems optical signals Silicon modulators

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Compact and voltage-tunable surface plasmon polariton-based optical neural networks

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OPTICS LETTERS 2025年第4期50卷 1109-1112页

作者： Yang, Chengwang Wu, Yongli Zhong, Chengyan Wang, Xiang Li, Lingfei Guo, Junxiong Huang, Wen Liu, Yu Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Natl & Local Joint Engn Lab RF Integrat & Microass Nanjing 210023 Peoples R China Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China Zhejiang Univ Coll Integrated Circuits Hangzhou 310027 Peoples R China Zhejiang Univ Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 310027 Peoples R China Chengdu Univ Inst Adv Study Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China UESTC Chengdu Res Inst Chengdu 610207 Peoples R China Tsinghua Univ Sch Integrated Circuits Beijing 100084 Peoples R China

optical neural networks (ONNs) offer advantages in parallel processing, low power consumption, and high-speed operation. However, existing ONN designs face challenges in miniaturization, stability, tunability, and integration. This study proposes a graphene surface plasmon polariton (GSPP) waveguide switch array for all-optical neural networks. The design features a compact structure with a lateral area of only 0.045 mu m2. Numerical simulations show that within the 30.2 to 49.4 THz range, the transmission rate is tunable from 0 to 0.875, accurately simulating synaptic weights in ONNs. The compact switch array achieves a recognition accuracy of 93.83% on the CIFAR-10 dataset, demonstrating its potential for high-speed, low-power, and highly integrated neural network computing platforms. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： neural networks Numerical simulation optical computing optical neural systems optical properties Photonic integrated circuits

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Artificial neural Network With Photonic Reservoir for Multiclass Modulation Format Identification

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第9期43卷 4175-4182页

作者： von Huenefeld, Guillermo Kaveh, Mahdi Hopfmueller, Joseph Safari, Pooyan Seker, Enes Thomas, Rijil Aghaeipour, Mahtab Stahl, David Suckow, Stephan Bienstman, Peter Lemme, Max C. Fischer, Johannes Karl Schubert, Colja Freund, Ronald Fraunhofer Inst Telecommun Heinrich Hertz Inst D-10587 Berlin Germany Tech Univ Berlin D-10623 Berlin Germany ID Photon GmbH D-85579 Neubiberg Germany Amo GmbH D-52074 Aachen Germany Rhein Westfal TH Aachen D-52062 Aachen Germany Univ Ghent B-9000 Ghent Belgium

This paper builds upon our previous work on photonic reservoir computing, presenting an enhanced artificial neural network (NN) integrated on a photonic integrated circuit (PIC) for the intelligent monitoring of optical signals. The NN employs the reservoir computing paradigm on optical signals, with a photonic reservoir and a digital readout for modulation format identification (MFI). Our focus is on the identification of dual-polarized wavelength-division multiplexing (WDM) signals with symbol rates between 32 and 35 GBd, covering modulation formats including 4QAM and 16QAM. Experimental results demonstrate the system's robust MFI capabilities, achieving high accuracy across various signal formats. These findings highlight the potential of integrated photonic NNs in enhancing the performance and efficiency of optical communication systems.

关键词： Photonics Integrated optics optical waveguides Reservoirs Artificial neural networks optical transmitters optical fibers optical device fabrication optical amplifiers optical receivers energy efficiency modulation format identification multiclass classifier neuromorphic photonics reservoir computing softmax wavelength-division multiplexing

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Efficient training of unitary optical neural networks

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OPTICS EXPRESS 2023年第24期31卷 39616-39623页

作者： Lu, Kunrun Guo, Xianxin Harbin Inst Technol Sch Sci Weihai 264209 Shandong Peoples R China Univ Oxford Clarendon Lab Parks Rd Oxford OX1 3PU England Lurtis Ltd Wood Ctr Innovat Quarry Rd Oxford OX3 8SB England

Deep learning has profoundly reshaped the technology landscape in numerous scientific areas and industrial sectors. This technology advancement is, nevertheless, confronted with severe bottlenecks in digital computing. optical neural network presents a promising solution due to the ultra-high computing speed and energy efficiency. In this work, we present systematic study of unitary optical neural network (UONN) as an approach towards optical deep learning. Our results show that the UONN can be trained to high accuracy through special unitary gradient descent optimization, and the UONN is robust against physical imperfections and noises, hence it is more suitable for physical implementation than existing ONNs.

关键词： Deep learning Information processing Machine learning neural networks optical amplifiers optical neural systems

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Fundamentals and recent developments of free-space optical neural networks

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JOURNAL OF APPLIED PHYSICS 2024年第3期136卷 030701-030701页

作者： McNeil, Alexander Montes Li, Yuxiao Zhang, Allen Moebius, Michael Liu, Yongmin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Charles Stark Draper Lab Cambridge MA 02139 USA Charles Stark Draper Lab Cambridge MA 02139 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

关键词： optical data processing

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Integrated convolutional kernel based on two-dimensional photonic crystals

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OPTICS LETTERS 2024年第21期49卷 6297-6300页

作者： Li, Daxing Zhang, Kuo Hu, Xiaoyong Feng, Shuai Minzu Univ China Sch Sci Beijing 100081 Peoples R China Minzu Univ China Optoelect Res Ctr Beijing 100081 Peoples R China Minist Educ Engn Res Ctr Photon Design Software Beijing 100081 Peoples R China Peking Univ State Key Lab Mesoscop Phys Beijing 100871 Peoples R China Peking Univ Collaborat Innovat Ctr Quantum Matter Nano Optoelectron Frontier Ctr Dept PhysMinist Educ Beijing 100871 Peoples R China Shanxi Univ Collaborat Innovat Ctr Extreme Opt Taiyuan 030006 Peoples R China Peking Univ Yangtze Delta Inst Optoelect Nantong 226010 Peoples R China

optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： neural networks optical computing optical network architecture optical neural systems Photonic crystals Refractive index

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Unlocking the Potential of Spiking neural networks: Understanding the What, Why, and Where

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第5期16卷 1648-1663页

作者： Wickramasinghe, Buddhi Chowdhury, Sayeed Shafayet Kosta, Adarsh Kumar Ponghiran, Wachirawit Roy, Kaushik Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Spiking neural networks (SNNs) are a promising avenue for machine learning with superior energy efficiency compared to traditional artificial neural networks (ANNs). Recent advances in training and input encoding have put SNNs on par with state-of-the-art ANNs in image classification. However, such tasks do not utilize the internal dynamics of SNNs fully. Notably, a spiking neuron's membrane potential acts as an internal memory, merging incoming inputs sequentially. This recurrent dynamic enables the networks to learn temporal correlations, making SNNs suitable for sequential learning. Such problems can also be tackled using ANNs. However, to capture the temporal dependencies, either the inputs have to be lumped over time (e.g., Transformers);or explicit recurrence needs to be introduced [e.g., recurrent neural networks (RNNs) and long-short-term memory (LSTM) networks], which incurs considerable complexity. To that end, we explore the capabilities of SNNs in providing lightweight solutions to four sequential tasks involving text, speech, and vision. Our results demonstrate that SNNs, by leveraging their intrinsic memory, can be an efficient alternative to RNNs and LSTMs for sequence processing, especially for certain edge applications. Furthermore, SNNs can be combined with ANNs (hybrid networks) synergistically to obtain the best of both worlds in terms of accuracy and efficiency.

关键词： Neurons Training Task analysis Encoding Membrane potentials Computational modeling Recurrent neural networks Automatic speech recognition neuromorphic computing optical flow estimation spiking neural networks (SNNs)

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optical convolution operations with optical neural networks for incoherent color image recognition

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OPTICS AND LASERS IN ENGINEERING 2025年 185卷

作者： Kim, Beomju Kim, Yelim Kim, Minjoo Yang, Sung Ik Jeong, Doo Seok Il Park, Won Hanyang Univ Div Mat Sci & Engn Seoul 04763 South Korea Kyung Hee Univ Dept Appl Chem Yongin 17104 South Korea

This study introduces optical neural networks (ONNs) designed to accelerate optical convolution operations using a red, green, and blue (RGB) pixel array integrated into conventional display technology. In a proof-ofconcept demonstration, we initially employed a rank-4 kernel for a normal convolution network, which was integrated with a fully connected layer, to accurately classify color images across five fruit categories. Following seven epochs of on-system iterative training for a 3,000 training dataset, the ONN achieved 96% classification accuracy and maintained robust performance on an unseen 1,000 test dataset. Our analysis also showed its potential for efficient operation, with a classification accuracy exceeding 94% using an average less than 34 aJ of optical energy per MAC operation. Additionally, we demonstrated depth-wise convolution with a rank-3 kernel, recurring the system to spectrally resolve the signals into independent R, G, and B channels. This architecture enabled the successful classification of complex patterns containing three MNIST handwritten digits encoded in RGB. Our strategy contributes significantly to optical computing and neuromorphic vision, facilitating efficient recognition of real-world, multi-color, and incoherent light images.

关键词： optical neural networks (ONNs) optical convolution operations Color image recognition Energy efficient recognition

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Photonic neuromorphic architecture for tens-of-task lifelong learning

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Light(Science & Applications) 2024年第3期13卷 519-530页

作者： Yuan Cheng Jianing Zhang Tiankuang Zhou Yuyan Wang Zhihao Xu Xiaoyun Yuan Lu Fang Sigma Laboratory Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Institute for Brain and Cognitive Science Tsinghua University(THUIBCS)Beijing 100084China

Scalable,high-capacity,and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning *** electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls,hindering them from the sustainable performance improvement and iterative multi-task *** to another modality of light,photonic computing has been progressively applied in high-efficient neuromorphic ***,we innovate a reconfigurable lifelong-learning optical neural network(L2 ONN),for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware *** from the inherent sparsity and parallelism in massive photonic connections,L2 ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field,while incrementally acquiring expertise on various tasks by gradually enlarging the *** multi-task optical features are parallelly processed by multi-spectrum representations allocated with different *** evaluations on freespace and on-chip architectures confirm that for the first time,L2 ONN avoided the catastrophic forgetting issue of photonic computing,owning versatile skills on challenging tens-of-tasks(vision classification,voice recognition,medical diagnosis,etc.)with a single ***,L2 ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks,and 14×larger capacity than existing optical neural networks while maintaining competitive performance on each individual *** proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme,permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

关键词： life neural versatile

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Reconfigurable binary diffractive optical neural network based on chalcogenide phase change material Ge2Sb2Se4Te1

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OPTICS EXPRESS 2024年第23期32卷 41433-41444页

作者： Fu, Ziwei Fu, Tingzhao Wu, Hao Zhu, Zhihong Zhang, Jianfa Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Hunan Prov Key Lab Novel NanoOptoelect Informat Ma Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Nanhu Laser Lab Changsha 410073 Peoples R China

Diffractive optical neural networks (DONNs) possess unique advantages such as light-speed computing, low energy consumption, and parallel processing, which have obtained increasing attention in recent years. However, once conventional DONNs are fabricated, their function remains fixed, which greatly limits the applications of DONNs. Thus, we propose a reconfigurable DONN framework based on a repeatable and non-volatile phase change material Ge2Sb2Se4Te1(GSST). By utilizing phase modulation units made of GSST to form the network's neurons, we can flexibly switch the functions of the DONN. Meanwhile, we apply a binary training algorithm to train the DONN weights to binary values of 0 and pi , which is beneficial for simplifying the design and fabrication of DONN while reducing errors during physical implementation. Furthermore, the reconfigurable binary DONN has been trained as a handwritten digit classifier and a fashion product classifier to validate the feasibility of the framework. This work provides an efficient and flexible control mechanism for reconfigurable DONNs, with potential applications in various complex tasks. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Holographic displays Machine learning neural networks optical computing optical neural systems Phase modulation

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