当前位置: X-MOL 学术Nanophotonics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Neural network-assisted meta-router for fiber mode and polarization demultiplexing
Nanophotonics ( IF 6.5 ) Pub Date : 2024-09-05 , DOI: 10.1515/nanoph-2024-0338
Yu Zhao 1, 2 , Huijiao Wang 1 , Tian Huang 1 , Zhiqiang Guan 3, 4 , Zile Li 1, 2 , Lei Yu 1 , Shaohua Yu 2 , Guoxing Zheng 1, 2, 4
Affiliation  

Advancements in computer science have propelled society into an era of data explosion, marked by a critical need for enhanced data transmission capacity, particularly in the realm of space-division multiplexing and demultiplexing devices for fiber communications. However, recently developed mode demultiplexers primarily focus on mode divisions within one dimension rather than multiple dimensions (i.e., intensity distributions and polarization states), which significantly limits their applicability in space-division multiplexing communications. In this context, we introduce a neural network-assisted meta-router to recognize intensity distributions and polarization states of optical fiber modes, achieved through a single layer of metasurface optimized via neural network techniques. Specifically, a four-mode meta-router is theoretically designed and experimentally characterized, which enables four modes, comprising two spatial modes with two polarization states, independently divided into distinct spatial regions, and successfully recognized by positions of corresponding spatial regions. Our framework provides a paradigm for fiber mode demultiplexing apparatus characterized by application compatibility, transmission capacity, and function scalability with ultra-simple design and ultra-compact device. Merging metasurfaces, neural network and mode routing, this proposed framework paves a practical pathway towards intelligent metasurface-aided optical interconnection, including applications such as fiber communication, object recognition and classification, as well as information display, processing, and encryption.

中文翻译:


用于光纤模式和极化解复用的神经网络辅助元路由器



计算机科学的进步将社会推向了一个数据爆炸的时代,其特点是对增强数据传输能力的迫切需求,特别是在用于光纤通信的空分复用和解复用设备领域。然而,最近开发的模式解复用器主要关注一维而不是多维内的模分(即强度分布和极化态),这极大地限制了它们在空分复用通信中的适用性。在这种情况下,我们引入了一种神经网络辅助的超路由器来识别光纤模式的强度分布和极化状态,这是通过神经网络技术优化的单层超表面实现的。具体来说,理论设计了一种四模元路由器并进行了实验表征,实现了四种模式,包括两种具有两种极化态的空间模式,独立划分为不同的空间区域,并成功被相应空间区域的位置识别。我们的框架为光纤模式解复用装置提供了一种范例,其特点是应用兼容性、传输容量和功能可扩展性,具有超简单的设计和超紧凑的设备。该框架融合了超表面、神经网络和模式路由,为智能超表面辅助光学互连铺平了一条实用途径,包括光纤通信、物体识别和分类以及信息显示、处理和加密等应用。
更新日期:2024-09-05
down
wechat
bug