Nonreciprocal spin waves in out-of-plane magnetized waveguides reconfigured by domain wall displacements

TL;DR

This paper demonstrates how out-of-plane magnetized spin-wave waveguides with reconfigurable domain walls can be used to create nonreciprocal, tunable, and reconfigurable spin-wave devices for advanced wave-based computing and signal routing.

Contribution

It introduces a novel reconfigurable spin-wave device utilizing domain wall displacements for nonreciprocal signal control and demonstrates its potential for wave-based computing platforms.

Findings

Spin-wave nonreciprocity enables unidirectional information transport.

Domain wall displacement allows non-volatile reconfiguration of device properties.

The device functions as a waveguide crossing, splitter, isolator, and multiplexer.

Abstract

Wave-based platforms for novel unconventional computing approaches like neuromorphic computing require a well-defined, but adjustable flow of wave information combined with non-volatile data storage elements to implement weights which allow for training and learning. Due to their inherent nonreciprocal properties and their direct physical interaction with magnetic data storage, spin waves are ideal candidates to realize such platforms. In the present study, we show how spin-wave nonreciprocity induced by dipolar interactions of nanowaveguides with antiparallel, out-of-plane magnetization orientations can be used to create a spin-wave circulator allowing for unidirectional information transport and complex signal routing. In addition, the device can be reconfigured by a magnetic domain wall with adjustable position, which allows for a non-volatile tuning of the nonreciprocity and signal propagation. These properties are demonstrated for a spin-wave directional coupler through a combination of micromagnetic simulations and analytical modeling also showing that it functions as a waveguide crossing element, tunable power splitter, isolator, and frequency multiplexer. As magnetic material, out-of-plane magnetized Bismuth-doped Yttrium Iron Garnet has been considered. For this material, the motion of domain walls by magnonic spin transfer torque has been recently experimentally demonstrated which enables to store results from spin-wave computation. In combination with the presented concept of domain wall based reconfiguration and nonlinear spin-wave dynamics, this enables for the creation of a nano-scaled nonlinear wave computing platform with the capability for self-learning.