基于新奇物理现象的智能光子芯片  

Smart photonic chips based on novel physical phenomena

作　　者：黄海琪 王芋霏 胡小永[1,2] HUANG Hai-Qi;WANG Yu-Fei;HU Xiao-Yong(State Key Laboratory of Artificial Microstructure and Mesoscopic Physics,School of Physics,Peking University,Beijing 100871,China;Collaborative Innovation Center of Extreme Optics,Shanxi University,Taiyuan 030006,China)

机构地区：[1]北京大学物理学院人工微结构和介观物理国家重点实验室,北京100871 [2]山西大学极端光学协同创新中心,太原030006

出　　处：《物理》2024年第1期12-21,共10页Physics

摘　　要：近年来,新材料结构、新理论观点、新器件架构等各个前沿方向的通力合作促使了智能光子学的迅猛发展。具有优异非线性特性的光学平台奠定了光子芯片丰富性能的基础,非厄米拓扑光子学等新理论的引入为高性能片上处理方案开辟了新的可能性,各类光学神经网络架构的新器件也为全光计算及全光大规模集成提供了有效的实现途径。基于上述背景,文章介绍了近年来对于智能光子学发展至关重要的非线性材料、非厄米拓扑光子学以及基于光学神经网络的新器件架构等领域的研究进展,并指出相关研究的发展对于未来更大规模、更高集成度、更复杂计算性能的智能光子芯片的实现将产生深远影响,在全光计算、全光信号处理、量子技术等领域有广泛的应用前景。In recent years the rapid development of intelligent photonics has relied on the collaborative efforts in various cutting-edge directions,including new material structures,new theoretical perspectives,and new device architectures.Optical platforms with excellent nonlinear characteristics have laid the foundation for the rich performance of photonic chips.The introduction of new theories such as non-Hermitian topological photonics has opened up new possibilities for high-performance on-chip processing solutions.New devices for various optical neural network architectures have provided effective pathways for all-optical computation and large-scale integration.Against this backdrop,this review assesses recent research progress in critical areas of intelligent photonics development,including nonlinear materials,non-Hermitian topological photonics,and novel device architectures based on optical neural networks.These developments will have profound implications for the large scale realization of future intelligent photonic chips with higher integration and more complex computational performance,promising broad applications in fields such as all-optical computing,all-optical signal processing,and quantum technologies.

关 键 词：光子芯片 非线性光学 非厄米拓扑光子学 光子神经网络 

分 类 号：TN40[电子电信—微电子学与固体电子学] TN256