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IEEE SPECTRUM 1972年第12期9卷 24-+页

作者： STROKE, GW State University of New York USA

Dozens of research workers in more than a hundred leading laboratories of the world are working on creating the foundations for a new field of engineering that promises to have a revolutionary impact on almost every field of science and technology. Resulting from the introduction of a variety of new techniques and devices-including the laser-as well as from the development of new photo-sensitive and electron-beam recording materials-including thermoplastics, codable films, and mass-storage holographic memories-this new technology, known as optical computing, is based upon mathematical concepts known as coherent or Fourier optics and holography. In terms of future developments and applications, the most dramatic results very likely will emerge from the implementation of real-time image processing in various forms. But the great power of optical computing derives primarily from its newly recognized capability of parallel processing, a natural property of the lens! In a general way, all aspects of this new field can be characterized by established concepts of electric and electronic signal processing and communications.

关键词： optical computing Holographic optical components optical recording optical films optical signal processing Laboratories Materials science and technology Laser theory Holography optical devices

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optical computing

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APPLIED MATHEMATICS AND COMPUTATION 2009年第4期215卷 1417-1430页

作者： Woods, Damien Naughton, Thomas J. Univ Seville Dept Comp Sci & Artificial Intelligence Seville Spain Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland Univ Oulu RFMedia Lab Oulu So Inst Ylivieska 84100 Finland

In this survey we consider optical computers that encode data using images and compute by transforming such images. We give an overview of a number of such optical computing architectures, including descriptions of the type of hardware commonly used in optical computing, as well as some of the computational efficiencies of optical devices. We go on to discuss optical computing from the point of view of computational complexity theory, with the aim of putting some old, and some very recent, results in context. Finally, we focus on a particular optical model of computation called the continuous space machine. We describe some results for this model including characterisations in terms of well-known complexity classes. (C) 2009 Elsevier Inc. All rights reserved.

关键词： optical computing optical algorithm optical implementation Continuous space machine Computational complexity Fourier transform Search algorithm

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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

Optical computing

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International Workshop on Physics and Computation

关键词： optical computing optical algorithm optical implementation Continuous space machine Computational complexity Fourier transform Search algorithm

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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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Concept of an in-orbit AI-system based on optical computing

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CEAS SPACE JOURNAL 2025年 1-13页

作者： Kuebler, Felix Yang, Mingwei Mannteuffel, Lennart Akyuez, Okan Wolters, Janik Stoll, Enrico Tech Univ Berlin Raumfahrttechn Marchstr 12-14 D-10587 Berlin Germany Deutsch Zentrum Luft & Raumfahrt Inst Opt Sensor Syst Rutherfordstr 2 D-12489 Berlin Germany

Machine Learning (ML), or Artificial Intelligence (AI) in general, is among today's fastest-growing methods to handle complex or computationally intensive tasks. ML is commonly implemented with Artificial Neural Networks (ANNs) on conventional computer systems that can limit their full potential. Even with access to specialized hardware such as graphics cards or Tensor Processing Units (TPUs), the demand for more computing power constantly increases. Although these hardware requirements can be met for terrestrial applications, an extraterrestrial or in-orbit application is considerably more challenging. Additional requirements for energy budget, thermal control, and radiation resistance can usually not be met, especially for small spacecraft. The benefits of an AI system for fast onboard data processing would, however, be remarkable. An optical approach to this problem can potentially be the solution. optical computers promise to be much more energy efficient and better suitable for the mentioned space requirements. An implementation of an optical computing device on a spacecraft has not been done and can be considered as a technological leap. This work, along with the project optical computing for Machine Learning in Orbit (OMLO) of the Technical University of Berlin (TU-Berlin), aims to specify and conceptualize such a system.

关键词： Artificial intelligence Machine learning optical computing Hybrid computing system Satellite payload Payload concept

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Research on atmospheric coherence length detection method based on optical computing lidar

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Optics Express 2025年第10期33卷 21055-21069页

作者： Shenbo Li Jie Wang Kee Yuan Dongfeng Shi Jian Huang Yafeng Chen Runbo Jiang Yingjian Wang Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine Mechanics HFIPS Chinese Academy of Sciences Hefei 230031 China Nanhu Laser Laboratory National University of Defense Technology Changsha 410073 China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine Mechanics HFIPS Chinese Academy of Sciences Hefei 230031 China

The atmospheric coherence length is an important parameter that reflects the turbulence effects on optical wave transmission through the atmosphere. Real-time acquisition of atmospheric coherence length plays a significant role in various fields. Utilizing DIM lidar, we integrated related imaging and lidar technologies, substituting high quantum efficiency array detectors with optical modulators and APD detectors. We then conducted research on atmospheric coherence length detection using optical computing lidar. Through the laser atmospheric turbulence phase screen transmission program, we conducted numerical simulations of the centroid jitter variance in non-imaging optical computing methods, which demonstrated good consistency with results calculated by conventional methods. The system structure and technical specifications of the independently developed optical computing lidar are introduced. Experiments on the high spatial-temporal distribution of atmospheric coherence length were conducted in Hefei during summer nights at multiple angles (15°, 30°, 45°, and 90°). Preliminary results indicate that the atmospheric coherence length near the ground diminishes gradually with increasing detection distance, up to 4.5 km. The numerical range observed is between 4 to 9 cm, suggesting that employing optical computing lidar to detect atmospheric coherence length is both feasible and reliable.

关键词： Atmospheric turbulence Detector arrays Imaging systems Numerical simulation optical computing optical detection

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optical computing

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Applied Optics 1978年第11期17卷 A137-1844页

作者： A. W. PRYCE V. S. HEWITSON Office of Naval Research Branch Office London

No abstract available.

关键词： Digital image processing Image processing optical computing optical systems Pattern recognition Speckle interferometry

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optical computing metasurfaces: applications and advances

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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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