发表机构
Ben-Gurion University of the Negev; Lancaster University（内盖夫本-古里安大学; 兰卡斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种集成片上真时延元件的光电储备池计算系统，利用氮化硅螺旋延迟线实现无错误波形分类和混沌预测，并引入相位编码方法提升性能。
AI 中文摘要
紧凑的延迟元件仍然是光子神经形态处理器中的一个核心挑战。在此，我们展示了一种光电延迟反馈储备池计算机，其反馈回路中集成了代工厂制造的氮化硅（Si$_3$N$_4$）真时延电路。八个级联的阿基米德螺旋提供了1.76米的片上光路，并计算出11.63纳秒的无源群延迟。在反馈增益$G=0.0223$时，系统无错误地分类正弦和方波波形（词错误率，WER$=0$，覆盖所有交叉验证折），预测Mackey-Glass混沌序列的最佳折归一化均方误差（NMSE）为0.0082，并执行九类日语元音说话人分类，WER$=0.0898$。我们还引入了一种子载波相位编码方法，通过刻意混叠将计算出的0.232$\tau$螺旋到参考相位对比映射到测量的储备池状态，在未增加插入损耗限制的反馈增益的情况下实现了NMSE$=0.0767$和WER$=0$。这些结果表明，Si$_3$N$_4$中的片上传播延迟可以作为光电储备池计算机的功能组件，并激励更低损耗、更集成的实现。
英文摘要
Compact delay elements remain a central challenge in photonic neuromorphic processors. Here, we demonstrate an optoelectronic delayed-feedback reservoir computer that incorporates a foundry-fabricated silicon nitride (Si$_3$N$_4$) true-time-delay circuit within its feedback loop. Eight cascaded Archimedean spirals provide a \SI{1.76}{\metre} on-chip optical path and a calculated passive group delay of \SI{11.63}{\nano\second}. At a feedback gain of $G=0.0223$, the system classifies sinusoidal and square waveforms without error (word error rate, WER\,$=$\,0 across all cross-validation folds), predicts the Mackey--Glass chaotic series with a best-fold normalized mean-square error (NMSE) of $0.0082$, and performs nine-class Japanese Vowels speaker classification with WER\,$=$\,0.0898. We also introduce a subcarrier phase-encoding method that maps the calculated $0.232π$ spiral-to-reference phase contrast onto the measured reservoir states through deliberate aliasing, achieving NMSE\,$=$\,0.0767 and WER\,$=$\,0 without increasing the insertion-loss-limited feedback gain. These results show that on-chip propagation delay in Si$_3$N$_4$ can operate as a functional component of an optoelectronic reservoir computer and motivate lower-loss, more-integrated implementations.
