Dataset for the publication:"Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics"

Dataset belonging to the manuscript: "Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics"

The text files "...Note.txt" corresponding to a figure number explains the relevant parameters from the data. 

Abstract: 

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal processing, filtering, and Boolean logic, they allow for hardware implementation of neural networks exploiting cascaded magnon scattering on the nanoscale. A game-changing boost is expected if nonvolatile magnon-signal storage and in-memory computation schemes become realistic. We outline recent progress in experimental research and micromagnetic modeling toward these goals before sketching remaining challenges.

本数据集隶属于学术论文《面向低功耗磁子学的非易失性磁子信号存储与内存内计算展望》。
与各图编号对应的“...Note.txt”文本文件，将对该数据中的相关参数进行说明。

摘要：
磁子（magnon）是自旋波（spin wave）的量子化单元，可通过磁有序材料传递角动量。其无需电荷流动即可分发与调控片上GHz频段信号，从而避免焦耳热损耗。除可应用于多路信号处理、滤波与布尔逻辑运算外，磁子还可依托纳米级级联磁子散射效应，实现神经网络的硬件部署。若非易失性磁子信号存储与内存内计算方案能够走向实用化，将为该领域带来革命性突破。本文概述了该方向在实验研究与微磁建模领域的近期进展，并梳理了当前尚存的挑战。