Tunable power-phase distributions in a phonon-magnon-coupled magnon microwave antenna for reservoir computing

Exploring the power and phase profiles of spin waves not only enhances our fundamental understanding of magnetic materials but also opens up avenues for energy-efficient technologies such as spintronics, magnonics, and potentially reservoir computing. Here, we present the power-phase distributions and their tunability of a surface-acoustic-wave-driven ``magnon microwave antenna'' (MMA), comprising patterned arrays of magnetostrictive nanomagnets embedded in piezoelectric heterostructures. The MMA generates tunable microwave frequencies without external bias fields, thanks to phonon-magnon coupling, producing multimode microwave frequencies with nonvolatile spin textures. A comprehensive static magnetic study elucidates the crucial role of the demagnetization energy distribution, rather than its overall magnitude in magnetization reversal processes. Additionally, functional tunability could be achieved through amplitude-dependent training using various combinations of nanowire and nanodot dimensions, topologies, material properties, and array configurations. The nonvolatile nature of the spin textures generated in the MMA under bias-field-free conditions is promising for energy-efficient logic and low-power computing applications. Thus this work introduces a novel alternative approach, paving the way to utilize these MMAs for on-chip reservoir computing, where amplitude varies at the operating frequency.

Authors

Institutions

Publication Details

Journal
Physical Review Applied
Published
2024-11-26
DOI
https://doi.org/10.1103/physrevapplied.22.054076
Citations
3
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tunable power-phase distributions in a phonon-magnon-coupled magnon microwave antenna for reservoir computing

Saibal Roy, Arindam Samanta
3 citations
Physical Review Applied
Neural Networks and Reservoir Computing
1.04
article

Tunable power-phase distributions in a phonon-magnon-coupled magnon microwave antenna for reservoir computing

Saibal Roy, Arindam Samanta
article en
3 citations

Abstract

Exploring the power and phase profiles of spin waves not only enhances our fundamental understanding of magnetic materials but also opens up avenues for energy-efficient technologies such as spintronics, magnonics, and potentially reservoir computing. Here, we present the power-phase distributions and their tunability of a surface-acoustic-wave-driven ``magnon microwave antenna'' (MMA), comprising patterned arrays of magnetostrictive nanomagnets embedded in piezoelectric heterostructures. The MMA generates tunable microwave frequencies without external bias fields, thanks to phonon-magnon coupling, producing multimode microwave frequencies with nonvolatile spin textures. A comprehensive static magnetic study elucidates the crucial role of the demagnetization energy distribution, rather than its overall magnitude in magnetization reversal processes. Additionally, functional tunability could be achieved through amplitude-dependent training using various combinations of nanowire and nanodot dimensions, topologies, material properties, and array configurations. The nonvolatile nature of the spin textures generated in the MMA under bias-field-free conditions is promising for energy-efficient logic and low-power computing applications. Thus this work introduces a novel alternative approach, paving the way to utilize these MMAs for on-chip reservoir computing, where amplitude varies at the operating frequency.

Physical Review AppliedVol. 22(5)
University College Cork (IE), Tyndall Centre (GB)
Science Foundation Ireland
Affordable and clean energy
Openalex Percentile: Top 18%
Neural Networks and Reservoir Computing
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.