On-chip metamaterial-enabled high-order mode-division multiplexing
On-chip metamaterial-enabled high-order mode-division multiplexing
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DOI：
10.1117/1.ap.5.5.056008
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发表时间：
2023-09
影响因子：
17.3
通讯作者：
Yu He;Xingfeng Li;Yong Zhang;Shaohua An;Hongwei Wang;Zhen Wang;Huifen. Chen;Yetian Huang;Hanzi Huang;N. Fontaine;R. Ryf;Yuhan Du;Lu Sun;Xingchen Ji;Xuhan Guo;Yingxiong Song;Qian Zhang;Yikai Su
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文献类型：
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作者：
Yu He;Xingfeng Li;Yong Zhang;Shaohua An;Hongwei Wang;Zhen Wang;Huifen. Chen;Yetian Huang;Hanzi Huang;N. Fontaine;R. Ryf;Yuhan Du;Lu Sun;Xingchen Ji;Xuhan Guo;Yingxiong Song;Qian Zhang;Yikai Su
抽象的。模分复用(MDM)技术能够使用正交波导模来构建并行数据流，从而实现高带宽数据传输。然而，关于高阶模式的产生和支持的演示很少，这主要是由于固有的大的材料群速度色散(GVD)，这使得选择性地耦合不同阶的空间模式变得困难。通过引入梯度折射率超材料结构，我们展示了片上GVD工程的可行性，这使得MDM工艺具有健壮性和完全可伸缩性。我们展示了一种同时支持TE0-TE15模式的创纪录的高阶MDM器件。在16个模式通道上编码的40 Gbaud 16进制正交幅度调制信号提供了2.162 Tbit/S的净数据速率，这是迄今报道的片内单波长传输的最高数据速率。我们的方法可以有效地扩展MDM技术提供的通道数量，并促进大容量光互连、高维量子通信和大规模神经网络等对并行性需求很大的新兴研究领域的发展。
Abstract. 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 group-velocity 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.