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optical computing metasurfaces: applications and advances

NANOPHOTONICS 2024年第4期13卷 419-441页

作者： Zhou, Hongqiang Zhao, Chongli He, Cong Huang, Lingling Man, Tianlong Wan, Yuhong Beijing Univ Technol Sch Phys & Optoelect Engn Beijing 100124 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing Engn Res Ctr Mixed Real & Adv Display Beijing 100081 Peoples R China

Integrated photonic devices and artificial intelligence have presented a significant opportunity for the advancement of optical computing in practical applications. optical computing technology is a unique computing system based on optical devices and computing functions, which significantly differs from the traditional electronic computing technology. On the other hand, optical computing technology offers the advantages such as fast speed, low energy consumption, and high parallelism. Yet there are still challenges such as device integration and portability. In the burgeoning development of micro-nano optics technology, especially the deeply ingrained concept of metasurface technique, it provides an advanced platform for optical computing applications, including edge detection, image or motion recognition, logic computation, and on-chip optical computing. With the aim of providing a comprehensive introduction and perspective for optical computing metasurface applications, we review the recent research advances of optical computing, from nanostructure and computing methods to practical applications. In this work, we review the challenges and analysis of optical computing metasurfaces in engineering field and look forward to the future development trends of optical computing.

关键词： metasurface optical computing functional device

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optical computing with supercontinuum generation in photonic crystal fibers

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OPTICS EXPRESS 2025年第4期33卷 7852-7861页

作者： Muda, Azka maula iskandar Tegin, Ugur Koc Univ Dept Elect & Elect Engn Istanbul Turkiye

We present and study a nonlinear photonic neural network using photonic crystal fibers, leveraging femtosecond pulse supercontinuum generation for optical computing. Investigating its efficacy across machine learning tasks, we uncover the crucial impact of nonlinear pulse propagation dynamics on network performance. Our findings show that octave-spanning supercontinuum generation results in loss of dataset variety due to many-to-one mapping, and optimal performance requires balancing optical nonlinearity with dataset complexity. This study offers guidance for designing energy-efficient and high-performance photonic neural network architectures by explaining the interplay between nonlinear dynamics and optical computing.

关键词： Machine learning Neural networks optical computing optical network architecture optical neural systems Single mode fibers

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optical computing at the subwavelength scale : a multifunctional MIM plasmonic logic gate

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APPLIED OPTICS 2024年第24期63卷 6364-6370页

作者： Haque, Mohammad Ashraful Rahad, Rummanur Faruque, Md. Omar Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh BRAG Univ Opt & Photon Res Grp Dhaka Bangladesh

Plasmonic logic gates are important components in integrated photonics. They are essential for high-speed Boolean computations and data transmission. For this purpose, an all-optical ultra-compact plasmonic logic gate is proposed in this paper. The design uses the metal-insulator-metal (MIM) configuration and the propagation of surface plasmon polaritons. The structure comprises three rectangular input slots coupled to the output slot through a rectangular resonator positioned on a gold (Au) surface with a footprint of 1000 nm x 800 nm. The logic gate is numerically investigated using finite element method analysis. It has been demonstrated that the proposed design can operate as OR, XOR, NOT, and AND gates, and their transmission spectra for different input states are analyzed. This novel solution, to the best of our knowledge, excels in high contrast ratio values of 45.34 dB (OR), 18.29 dB (XOR), 18.29 dB (NOT), and 7.26 dB (AND). The resonant wavelengths of the logic gate are 821.21 nm (OR), 863.23 nm (XOR), 814.71 (NOT), and 787.69 nm (AND). The introduction of Au in this device significantly enhances its plasmonic properties, offering efficient light confinement, chemical stability, and a strong plasmonic response. This plasmonic logic gate not only expands the repertoire of integrated photonics components but also promises breakthroughs in high-speed data processing and communication technologies, positioning it as a crucial advancement in the field of nanophotonics. (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： Diffraction limit Effective refractive index Finite element method Integrated photonics optical computing Surface plasmon polaritons

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Resource-Saving and High-Robustness Image Sensing Based on Binary optical computing

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LASER & PHOTONICS REVIEWS 2025年第7期19卷

作者： Zhou, Zhanhong Li, Ziwei Zhou, Wei Chi, Nan Zhang, Junwen Dai, Qionghai Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Shanghai Engn Res Ctr Low Earth Orbit Satellite Commun & Applicat Shanghai 200433 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China

Computational imaging, as a novel technology utilizing encoded image acquisition, relies on intelligent decoding methods for effective image restoration and sensing. optical computing-based decoders can efficiently process and extract features from pre-sensor information, reducing the computational burden on digital computers. However, mainstream parallel optical neural network (ONN) architectures based on wavefront propagation typically possess complex network structures and high-precision parameters, which pose challenges in terms of precise fabrication and system calibration, as well as sensitivity to signal-to-noise ratios. In this work, a binary-weighted optical computing engine is proposed with spatial multiplexing and aggregation (B-OSMA), a large-scale passive ONN implementation that achieves high-efficiency image sensing. Employing B-OSMA as an optical decoder, demonstrated image categorizing from 2% compressive is experimented sampling with 92.0% and 83.8% accuracy on MNIST and fashion-MNIST datasets, respectively, approaching the performance of full-precision electronic computing while reducing storage requirements by 97%. Compared to conventional ONNs with analog weights, the B-OSMA exhibits enhanced resilience against systematic errors and ambient noise. This work represents a significant advancement towards practical applications of optical computing in image sensing. A novel binary-weighted optical computing implementation with spatial multiplexing and aggregation (B-OSMA) scheme achieves high-efficiency image sensing. Coupling with a light-weighted digital discriminator, B-OSMA demonstrates good performances that are competitive to full-precision electric computing on MNIST and Fashion-MNIST datasets, with greatly reduced storage consumption and promoted resilience against systematic errors and ambient noise. image

关键词： binary neural network compressive sensing image sensing optical computing optical neural network

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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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optical computing powers graph neural networks

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APPLIED OPTICS 2022年第35期61卷 10471-10477页

作者： Tang, Kaida Chen, Jianwei Jiang, Huaqing Chen, Jun Jin, Shangzhong Hao, Ran China Jiliang Univ Coll Opt & Elect Technol Hangzhou 310020 Peoples R China

Graph-based neural networks have promising perspectives but are limited by electronic bottlenecks. Our work explores the advantages of optical neural networks in the graph domain. We propose an optical graph neural network (OGNN) based on inverse-designed optical processing units (OPUs) to classify graphs with optics. The OPUs, combined with two types of optical components, can perform multiply-accumulate, matrix-vector multi-plication, and matrix-matrix multiplication operations. The proposed OGNN can classify typical non-Euclidean MiniGCDataset graphs and successfully predict 1000 test graphs with 100% accuracy. The OPU-formed optical- electrical graph attention network is also scalable to handle more complex graph data, such as the Cora dataset, with 89.0% accuracy.(c) 2022 Optica Publishing Group

关键词： Neural networks optical components optical computing optical networks optical neural systems optical processing

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Multiplexable all-optical nonlinear activator for optical computing

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OPTICS EXPRESS 2024年第10期32卷 18161-18174页

作者： Teng, Caihong Zhang, Xuhao Tang, Jindao Ren, Aobo Deng, Guangwei Wu, Jiang Wang, Zhiming Univ Elect Sci & Technol China UESTC Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all -optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth ( similar to 11.24 GHz), low nonlinear threshold ( similar to 2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all -optical neural networks.

关键词： Machine learning Neural networks Numerical simulation optical computing optical limiting optical neural systems

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Time-varying materials for analog optical computing

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JOURNAL OF optical TECHNOLOGY 2024年第5期91卷 293-299页

作者： Levkovskaya, Valeriya m. Kharitonov, Anton V. Kharintsev, Sergey s. Kazan Fed Univ Kazan Russia

Subject of study. In this study, a new class of artificial electromagnetic media, known as time-varying materials, is explored. These materials are characterized by a rapid modulation of their optical parameters on ultrashort timescales that are comparable to or shorter than the wave period. Aim of study. The aim of this study was to develop a new platform for implementing analog optical computers. Specifically, we investigated the dependence of the amplitudes and frequencies of waves, formed through interaction with a time-varying medium, on the permittivity switching time and magnitude of spectral dispersion. Method. The amplitudes of reflected and refracted electromagnetic waves were calculated using the time-domain finite-element method. A Lorentz model with a time-dependent plasma frequency was employed to analyze the time-varying medium with spectral dispersion. The optical response of the medium, which exhibited noninstantaneous switching, was studied by considering a sigmoidal switching profile. Main results. Our findings show that a dispersive time-varying medium can generate several spectral components that are shifted from the frequency of the incident light. By controlling the speed and depth of permittivity switching, the amplitudes and frequencies of the time-reflected/refracted waves can be adjusted. To observe the reflected wave, the duration of switching should be comparable to or less than the period of the incident wave. Practical significance. The results of this study are valuable for developing next- generation optical devices. The ability to flexibly control the amplitude and frequency of light waves through the temporal modulation of homogeneous media offers new opportunities for the realization of analog optical computers. (c) 2024 Optica Publishing Group

关键词： optical computing optical materials optical media optical properties Refractive index Spatial frequency

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optical computing of quantum revivals

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OPTICS EXPRESS 2022年第15期30卷 27180-27195页

作者： Maia, M. R. Jonathan, D. de Oliveira, T. R. Khoury, A. Z. Tasca, D. S. Univ Fed Fluminense Inst Fis BR-24210346 Niteroi RJ Brazil Inst Fed Fluminense BR-28360000 Bom Jesus Do Itabapoana RJ Brazil

Interference is the mechanism through which waves can be structured into the most fascinating patterns. While for sensing, imaging, trapping, or in fundamental investigations, structuredwaves play nowadays an important role and are becoming the subject of many interesting studies. Using a coherent optical field as a probe, we show how to structure light into distributions presenting collapse and revival structures in its wavefront. These distributions are obtained from the Fourier spectrum of an arrangement of aperiodic diffracting structures. Interestingly, the resulting interference may present quasiperiodic structures of diffraction peaks on a number of distance scales, even though the diffracting structure is not periodic. We establish an analogy with revival phenomena in the evolution of quantum mechanical systems and illustrate this computation numerically and experimentally, obtaining excellent agreement with the proposed theory. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Destructive interference Diffraction gratings Far field diffraction optical computing optical lattices X ray diffraction

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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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