光学神经网络研究进展  

Research Progress of Optical Neural Networks

作　　者：冯佳楠 胡建阳 张修建[2] 林杰[1] 金鹏[1] FENG Jianan;HU Jianyang;ZHANC Xiujan;LIN JiJIN Peng(Key Laboratory of Micro-systems and Micro-structures Manufacturing,Ministry of Education,Harbin Institute of Technology,Harbin 150001,China;Key Laboratory of Artificial Intelligence Measurement and Standards,Beijing Aerospace Institute for Metrology and Measurement Technology,Beijing 100076,China)

机构地区：[1]哈尔滨工业大学微系统与微结构制造教育部重点实验室,哈尔滨150001 [2]北京航天计量测试技术研究所人工智能计量测试与标准重点实验室,北京100076

出　　处：《宇航计测技术》2025年第2期49-62,共14页Journal of Astronautic Metrology and Measurement

基　　金：国家自然科学基金(U2341245,62175050)。

摘　　要：近年来,以深度学习为代表的人工智能技术快速发展,人工智能深度赋能传统行业,引领实现新一轮产业技术革命。然而,电子芯片晶体管的尺寸正逐步接近物理极限,导致传统电子神经网络无法满足指数增长的算力需求。受益于光子的独特优势,光计算技术将光电子技术与神经网络模型相结合,具有并行化、高速度、低功耗、多维度处理优势。文章对光学神经网络研究过程进行了梳理,重点对光学衍射神经网络的计算架构进行了分析讨论,最后总结了大规模光学衍射神经网络实用化面临的挑战,并对未来发展趋势进行了展望。Recently, artificial intelligence, particularly deep learning, has developed rapidly, which deeply empowers traditional industries and leads the realization of a new round of industrial technology revolution.However, the size of transistors on electronic chips is gradually approaching the physical limit, resulting in the inability of traditional electronic neural networks to meet the exponential increasing demand for computing power.Benefiting from the unique advantages of photon, optical computing technology merges optoelectronic technology with neural network models, which has the advantages of parallelization, high speed, low power consumption and multi-dimensional processing.The research progress of optical neural networks is reviewed, concentrating on the computational architecture of optical diffractive neural networks.The challenges facing the practical implementation of large-scale optical diffractive neural networks are analysed and summarized, and the future development trend is prospected.

关 键 词：光学信息处理 光计算 光学神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V443[自动化与计算机技术—控制科学与工程]