AI 中文总结
该研究提出基于异质磁性纳米环阵列的纳米磁储备池计算机，经实验验证其可降低预测误差，增强储备池表达能力，为可扩展磁性计算架构提供新途径。
AI 中文摘要
物理储备池计算利用物理系统固有的非线性和依赖历史的动力学特性，以最小的训练开销完成机器学习任务。本文介绍一种基于异质互连磁性纳米环阵列的纳米磁储备池计算机，结合多通道平面霍尔效应读出。该器件包含子阵列，其纳米环的轨道宽度从500 nm到300 nm系统变化，使单个储备池内可获得几何多样的磁性系统的异质动力学特性。通过将时变输入信号作为驱动旋转磁场的调制施加，评估纳米环储备池在非线性信号变换和Mackey-Glass时间序列预测任务上的性能。研究发现，与单通道读出相比，组合多个宽度依赖通道的输出可显著降低归一化均方根误差，最优通道组合取决于任务需求。这些结果表明，几何异质性提供了额外的、实验可及的自由度和互补的计算特性。主成分分析进一步揭示，相关特征的简化子集可捕获大部分计算相关信息，同时抑制噪声贡献。这些结果证明，可控几何异质性增强了储备池的表达能力，并为可扩展的磁性计算架构提供了途径，其中多输出磁性超材料可作为器件网络的可配置动态构建块。
英文摘要
Physical reservoir computing utilizes the intrinsic nonlinear and history-dependent dynamics of physical systems to perform machine-learning tasks with minimal training overhead. Here, we introduce a nanomagnetic reservoir computer based on a heterogeneous array of interconnected magnetic nanorings, combined with multi-channel planar Hall effect readout. The device comprises subarrays of rings with systematically varied track widths ranging from 500 nm to 300 nm, enabling access to the heterogeneous dynamics of geometrically diverse magnetic systems within a single reservoir. By applying time-varying input signals as modulations of a driving rotating magnetic field, we evaluate the nanoring reservoir's performance on nonlinear signal transformation and Mackey-Glass time-series prediction tasks. We find that combining outputs from multiple width-dependent channels significantly reduces the normalized root-mean-square error compared to single-channel readout, with the optimal channel combinations depending on task requirements. These results demonstrate that geometric heterogeneity provides an additional, experimentally accessible degree of freedom and complementary computational features. Principal component analysis further reveals that a reduced subset of correlated features captures most of the computationally relevant information while suppressing noise contributions. These results demonstrate that controlled geometric heterogeneity enhances reservoir expressivity and suggest a route toward scalable magnetic computing architectures in which multi-output magnetic metamaterials serve as configurable dynamical building blocks for device networks.
