Distinct memory properties in spin-wave reservoir computing based on synthetic antiferromagnet

TL;DR

This paper explores the use of synthetic antiferromagnets in spin-wave reservoir computing, revealing two distinct memory properties arising from acoustic and optical modes, which could enhance energy-efficient AI hardware.

Contribution

It introduces a novel application of synthetic antiferromagnets in spin-wave reservoir computing, demonstrating their unique memory capabilities through theoretical and numerical analysis.

Findings

Two distinct memory properties emerge in SAF-based spin-wave RC.

Acoustic and optical modes exhibit different spin-wave characteristics.

SAF devices show potential for energy-efficient AI hardware.

Abstract

Spin-wave-based physical reservoir computing (RC) is a promising candidate for energy-efficient physical implementations of artificial intelligence because of its potential for nanoscale integration with low power consumption. Most of the previous studies on spin-wave RC have utilized spin waves excited in a single-layer ferromagnet. In this study, we focused on spin waves in a synthetic antiferromagnet (SAF), consisting of two ferromagnetic layers coupled antiferromagnetically, and investigated additional memory properties of spin-wave RC. We theoretically and numerically demonstrate the emergence of two distinct memory properties in the SAF device due to the distinct spin-wave characteristics of the acoustic and optical modes inherent in SAFs.