Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning

Zhang Z, Lin K, Zhang Y, Bournel A, Xia K, Klaeui M, Zhao W (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Science Advances American Association for the Advancement of Science

Book Volume: 9

Article Number: eade7439

Journal Issue: 6

DOI: 10.1126/sciadv.ade7439

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.

Involved external institutions

Beihang University, Beijing University of Aeronautics and Astronautics (BUAA) / 北京航空航天大学 CN China (CN) École Polytechnique - Université Paris-Saclay FR France (FR) Southeast University / 东南大学 CN China (CN) Johannes Gutenberg-Universität Mainz (JGU) DE Germany (DE)

How to cite

APA:

Zhang, Z., Lin, K., Zhang, Y., Bournel, A., Xia, K., Klaeui, M., & Zhao, W. (2023). Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning. Science Advances, 9(6). https://dx.doi.org/10.1126/sciadv.ade7439

MLA:

Zhang, Zhizhong, et al. "Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning." Science Advances 9.6 (2023).

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