On-chip metamaterial-enabled high-order mode-division multiplexing

被引:0
作者
Yu He [1 ]
Xingfeng Li [1 ]
Yong Zhang [1 ]
Shaohua An [1 ]
Hongwei Wang [1 ]
Zhen Wang [1 ]
Haoshuo Chen [2 ]
Yetian Huang [3 ]
Hanzi Huang [3 ]
Nicolas KFontaine [2 ]
Roland Ryf [2 ]
Yuhan Du [1 ]
Lu Sun [1 ]
Xingchen Ji [4 ]
Xuhan Guo [1 ]
Yingxiong Song [3 ]
Qianwu Zhang [3 ]
Yikai Su [1 ]
机构
[1] Shanghai Jiao Tong University, Department of Electronic Engineering, State Key Laboratory of Advanced Optical Communication Systems and Networks
[2] Nokia Bell Labs
[3] Shanghai University, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication
[4] Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, John Hopcroft Center for Computer Science
关键词
D O I
暂无
中图分类号
O441.4 [电磁波与电磁场]; TN25 [波导光学与集成光学];
学科分类号
0809 ; 0702 ; 070207 ;
摘要
Mode-division multiplexing(MDM) technology enables high-bandwidth data transmission using orthogonal waveguide modes to construct parallel data streams. However, few demonstrations have been realized for generating and supporting high-order modes, mainly due to the intrinsic large material groupvelocity dispersion(GVD), which make it challenging to selectively couple different-order spatial modes.We show the feasibility of on-chip GVD engineering by introducing a gradient-index metamaterial structure,which enables a robust and fully scalable MDM process. We demonstrate a record-high-order MDM device that supports TE0–TE15 modes simultaneously. 40-GBaud 16-ary quadrature amplitude modulation signals encoded on 16 mode channels contribute to a 2.162 Tbit∕s net data rate, which is the highest data rate ever reported for an on-chip single-wavelength transmission. Our method can effectively expand the number of channels provided by MDM technology and promote the emerging research fields with great demand for parallelism, such as high-capacity optical interconnects, high-dimensional quantum communications, and large-scale neural networks.
引用
收藏
页码:111 / 118
页数:8
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