Lee Mu-Kun, Mochizuki Masahito
Department of Applied Physics, Waseda University, Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan.
Sci Rep. 2023 Nov 8;13(1):19423. doi: 10.1038/s41598-023-46677-w.
By performing numerical simulations for the handwritten digit recognition task, we demonstrate that a magnetic skyrmion lattice confined in a thin-plate magnet possesses high capability of reservoir computing. We obtain a high recognition rate of more than 88%, higher by about 10% than a baseline taken as the echo state network model. We find that this excellent performance arises from enhanced nonlinearity in the transformation which maps the input data onto an information space with higher dimensions, carried by interferences of spin waves in the skyrmion lattice. Because the skyrmions require only application of static magnetic field instead of nanofabrication for their creation in contrast to other spintronics reservoirs, our result consolidates the high potential of skyrmions for application to reservoir computing devices.
通过对手写数字识别任务进行数值模拟，我们证明了限制在薄板磁体中的磁斯格明子晶格具有很高的储层计算能力。我们获得了超过88%的高识别率，比作为回声状态网络模型的基线高出约10%。我们发现，这种优异的性能源于在将输入数据映射到更高维度信息空间的变换中增强的非线性，该变换由斯格明子晶格中的自旋波干涉携带。由于与其他自旋电子学储层相比，斯格明子的产生仅需要施加静磁场而非纳米制造，我们的结果巩固了斯格明子在储层计算设备应用方面的巨大潜力。
