Authors:	Zhang, Zhizhong Lin, Kelian Zhang, Yue Bournel, Arnaud Xia, Ke Kläui, Mathias Zhao, Weisheng
Title:	Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning
Online publication date:	19-Jun-2023
Year of first publication:	2023
Language:	english
Abstract:	Neuromorphic computing is expected to achieve human-brain performance by reproducing the structure of biological neural systems. However, previous neuromorphic designs based on synapse devices are all unsatisfying for their hardwired network structure and limited connection density, far from their biological counterpart, which has high connection density and the ability of meta-learning. Here, we propose a neural network based on magnon scattering modulated by an omnidirectional mobile hopfion in antiferromagnets. The states of neurons are encoded in the frequency distribution of magnons, and the connections between them are related to the frequency dependence of magnon scattering. Last, by controlling the hopfion’s state, we can modulate hyperparameters in our network and realize the first meta-learning device that is verified to be well functioning. It not only breaks the connection density bottleneck but also provides a guideline for future designs of neuromorphic devices.
DDC:	530 Physik 530 Physics
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 08 Physik, Mathematik u. Informatik
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9193
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY-NC
Information on rights of use:	https://creativecommons.org/licenses/by-nc/4.0/
Journal:	Science advances 9 6
Pages or article number:	eade7439
Publisher:	Assoc.
Publisher place:	Washington, DC u.a.
Issue date:	2023
ISSN:	2375-2548
Publisher DOI:	10.1126/sciadv.ade7439
Appears in collections:	JGU-Publikationen