论文标题
使用机器学习的动态手性磁纹理的拓扑表征
Topological characterization of dynamic chiral magnetic textures using machine learning
论文作者
论文摘要
最近提出的自旋设备使用磁性天空作为信息。这些手性磁对象的可靠检测是必不可少的要求。然而，磁空的高迁移率导致其在有限温度下的随机运动，从而阻碍了拓扑数的精确测量。在这里，我们证明了对人工神经网络的成功培训，以通过随时间融合的，尺寸减少的数据来重建狭窄的几何形状中的天空数量。我们的结果证明有可能从时间平均的测量中恢复拓扑费，并因此涂抹动态天空集合，这与实验结果的解释，基于Skyrmion的计算和内存概念直接相关
Recently proposed spintronic devices use magnetic skyrmions as bits of information. The reliable detection of those chiral magnetic objects is an indispensable requirement. Yet, the high mobility of magnetic skyrmions leads to their stochastic motion at finite temperatures, which hinders the precise measurement of the topological numbers. Here, we demonstrate the successful training of artificial neural networks to reconstruct the skyrmion number in confined geometries from time-integrated, dimensionally reduced data. Our results prove the possibility to recover the topological charge from a time-averaged measurement and hence smeared dynamic skyrmion ensemble, which is of immediate relevance to the interpretation of experimental results, skyrmion-based computing, and memory concepts
