发表机构
Institute for Cross-Disciplinary Physics and Complex Systems - IFISC (UIB-CSIC); Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria（跨学科物理与复杂系统研究所; 坎塔布里亚物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用少模光纤（FMF）结合半导体光放大器（SOA）的非线性，构建时空计算架构，在28.5 Gb/s速率下完成延迟异或和高阶奇偶校验等非线性任务，通过多时间样本特征提升分类性能。
AI 中文摘要
阶跃折射率少模光纤（FMF）通过利用模间色散将时间输入映射为具有短期记忆的时空表示，为高速光子信息处理提供了紧凑的无源平台。此前研究中，我们已利用这类FMF实现了线性分类任务，例如超快多比特头识别。本研究通过在色散介质输出端引入光学非线性，将这类架构的计算能力扩展至非线性任务求解。FMF的输出经并行的直接分支和非线性分支处理，其中非线性分支采用非线性半导体光放大器（SOA）实现。我们在28.5 Gb/s的速率下，针对两个延迟异或（XOR）和高阶奇偶校验任务对该架构进行实验评估，结果表明，结合直接分支与SOA变换后的表示可降低分类误差，并拓宽支持低误差分类的工作条件范围；此外，通过将每个比特周期内的多个时间样本作为独立分类器特征，我们提高了光子表示的维度，进而提升了计算性能。
英文摘要
Step-index few-mode fibers (FMFs) provide a compact passive platform for high-speed photonic information processing by exploiting modal dispersion to map temporal inputs into spatiotemporal representations with short-term memory. In previous studies, we have demonstrated linear classification tasks with such FMFs, such as ultrafast multibit header recognition. Here, we extend the computational capability of such architectures towards solving nonlinear tasks by introducing an optical nonlinearity at the output of the dispersive medium. The FMF output is processed through parallel direct and nonlinear branches, with the latter implemented using a nonlinear semiconductor optical amplifier (SOA). We experimentally evaluate the architecture at 28.5 Gb/s on two delayed XOR and higher-order parity tasks. We show that combining the direct and SOA-transformed representations reduces classification errors and broadens the range of operating conditions supporting low-error classification. In addition, by exploiting multiple temporal samples within each bit period as independent classifier features, we increase the dimensionality of the photonic representation and improve the computational performance.
