Multiferroic Magnon Spin-Torque Based Reconfigurable Logic-In-Memory
Yahong Chai, Yuhan Liang, Cancheng Xiao, Yue Wang, Bo Li, Dingsong, Jiang, Pratap Pal, Yongjian Tang, Hetian Chen, Yuejie Zhang, Witold, Skowro\'nski, Qinghua Zhang, Lin Gu, Jing Ma, Pu Yu, Jianshi Tang, Yuan-Hua, Lin, Di Yi, Daniel C. Ralph, Chang-Beom Eom, Huaqiang Wu

TL;DR
This paper demonstrates a reconfigurable magnon-based logic-in-memory device utilizing multiferroic materials, enabling electrical control of magnon transport and logic operations with potential for low-power, scalable in-memory computing.
Contribution
It introduces a novel magnon logic-in-memory device using multiferroic bismuth ferrite, enabling electrical manipulation and reconfigurability of magnon-based logic.
Findings
Magnon information can be encoded to ferromagnetic bits via spin torque.
Ferroelectric polarization modulates magnon spin-torque electrically.
Reconfigurable logic-in-memory operations are demonstrated in a single device.
Abstract
Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient electrical manipulation of magnon transport. Here we present a magnon logic-in-memory device in a spin-source/multiferroic/ferromagnet structure, where multiferroic magnon modes can be electrically excited and controlled. In this device, magnon information is encoded to ferromagnetic bits by the magnon-mediated spin torque. We show that the ferroelectric polarization can electrically modulate the magnon spin-torque by controlling the non-collinear antiferromagnetic structure in multiferroic bismuth ferrite thin films with coupled antiferromagnetic and ferroelectric orders. By manipulating the two coupled non-volatile state variables (ferroelectric…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Multiferroics and related materials · Advanced Memory and Neural Computing
