AI 中文总结
本研究提出一种基于磁光电子振荡器（MOEO）的物理储备池，通过光纤延迟线与自旋波四波非线性效应分别实现短期记忆与非线性，经STM、PC任务及数值模拟验证其性能良好。
AI 中文摘要
物理储备池计算是一种有前景的方法，可实现快速且高能效的计算机视觉、自然语言处理及通用模式识别。本研究提出一种基于磁光电子振荡器（MOEO）的物理储备池，该方法可将短期记忆与非线性实现为独立的系统组件。器件的光路采用光纤延迟线作为短期记忆元件，微波路径则负责将输入数据非线性映射至高维空间，这一过程由在钇铁石榴石（YIG）铁氧体薄膜中传播的自旋波的强四波非线性效应实现。储备池性能通过完成与任务无关的测试（即短期记忆（STM）任务和奇偶校验（PC）任务）进行评估，此外还开发了基于MOEO的储备池数值模型，储备池性能的数值模拟结果与实验数据吻合良好。
英文摘要
Physical reservoir computing is a promising approach for fast and energy efficient computer vision, natural language processing, and general pattern recognition. This work presents a physical reservoir based on magnonic-optoelectronic oscillator (MOEO). This approach allowed us to realize short-term memory and nonlinearity as separate system components. The device`s optical path uses a fiber-optic delay line as a short-term-memory element. The microwave path is responsible for nonlinear mapping of input data to a higher-dimensional space. The strong four-wave nonlinearity of spin waves propagating in an yttrium-iron garnet (YIG) ferrite film enables the process. The reservoir performance is evaluated by completing task-independent tests known as short-term memory (STM) and parity-check (PC) tasks. In addition, a numerical model of the MOEO based reservoir is developed. Results of the numerical simulation of the reservoir performance are in good agreement with the experimental data.
Journal refOptics Communications 616, 133313 (2026)
DOI：10.1016/j.optcom.2026.133313
