基于分层超对称变换的高维超模光子学
High-dimensional Supermode Photonics Enabled by Hierarchical Supersymmetric Transformation
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中文总结 AI 辅助
本文提出分层二阶离散超对称变换方法，实现高纯度超模激发提取，实验演示六超模复用系统，获低损耗、低串扰及1.2Tbit/s传输性能，为高维超模光子学提供可扩展方案。
中文摘要 AI 辅助
模式是光子信息处理的基本自由度，但常规多模波导呈现非等距有效折射率分布，使得间距紧密的模式易受模式间串扰影响。超模光子学可通过几何工程化耦合波导阵列实现大且等距的有效折射率间距以克服该局限，但亚波长尺度下精确的超模激发与探测仍具挑战性。本文报道一种分层二阶离散超对称（DSUSY）变换方法，该方法可在紧凑且可扩展的架构中实现任意目标超模的高纯度激发与提取。我们在绝缘体上硅及氮化硅平台上实验演示了六超模复用系统。得益于大的超模折射率间距与DSUSY变换的等谱性，制备的器件在100nm波长范围内所有通道均表现出低插入损耗（<2.6dB）与模式间串扰（<-11.1dB）。在硅器件上的高速传输实验实现了1.2Tbit/s的总数据速率，所有通道的误码率均低于7%的硬判决前向纠错阈值。该方法还可支持偏振不敏感架构，实现紧凑的偏振-超模混合架构。本研究为面向高容量光互连、高度并行AI光计算及高维量子信息处理的高维超模光子学提供了可扩展的途径。
英文摘要
Modes provide a fundamental degree of freedom for photonic information processing, yet conventional multimode waveguides exhibit non-equidistant effective-index distributions, making closely spaced modes vulnerable to intermodal crosstalk. Supermode photonics can overcome this limitation by geometrically engineering coupled waveguide arrays to realize large and equidistant effective-index spacing, but precise supermode excitation and detection remain challenging at the subwavelength scale. Here, we report a hierarchical second-order discrete supersymmetric (DSUSY) transformation method that enables high-purity excitation and extraction of arbitrary target supermodes in a compact and scalable architecture. We experimentally demonstrate six-supermode multiplexing systems on silicon-on-insulator and silicon nitride platforms. Benefiting from the large supermode index spacing and the isospectrality of DSUSY transformations, the fabricated devices exhibit low insertion losses (<2.6 dB) and intermodal crosstalk (<-11.1 dB) for all channels over a 100-nm wavelength range. A high-speed transmission experiment on the silicon device achieves an aggregate data rate of 1.2 Tbit/s, with all channel bit error rates below the 7% hard-decision forward-error-correction threshold. The method can further support polarization-insensitive architectures, enabling compact polarization-supermode hybrid multiplexing. This work provides a scalable route toward high-dimensional supermode photonics for high-capacity optical interconnects, highly parallel AI optical computing, and high-dimensional quantum information processing.
