Autoren:	Zhang, Zhizhong Lin, Kelian Zhang, Yue Bournel, Arnaud Xia, Ke Kläui, Mathias Zhao, Weisheng
Titel:	Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning
Online-Publikationsdatum:	19-Jun-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/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-Sachgruppe:	530 Physik 530 Physics
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 08 Physik, Mathematik u. Informatik
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9193
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY-NC
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by-nc/4.0/
Zeitschrift:	Science advances 9 6
Seitenzahl oder Artikelnummer:	eade7439
Verlag:	Assoc.
Verlagsort:	Washington, DC u.a.
Erscheinungsdatum:	2023
ISSN:	2375-2548
DOI der Originalveröffentlichung:	10.1126/sciadv.ade7439
Enthalten in den Sammlungen:	JGU-Publikationen