Diverse dynamics in interacting vortices systems through tunable conservative and non-conservative coupling strengths
Magnetic vortices are highly tunable, nonlinear systems with ideal properties for being applied in spin wave emission, data storage, and neuromorphic computing. However, their technological application is impaired by a limited understanding of non-conservative forces, that results in the open challenge of attaining precise control over vortex dynamics in coupled vortex systems. Here, we present an analytical model for the gyrotropic dynamics of coupled magnetic vortices within nano-pillar structures, revealing how conservative and non-conservative forces dictate their complex behavior. Validated by micromagnetic simulations, our model accurately predicts dynamic states, controllable through external current and magnetic field adjustments. The experimental verification in a fabricated nano-pillar device aligns with our predictions, and it showcases the system's adaptability in dynamical coupling. The unique dynamical states, combined with the system's tunability and inherent memory, make it an exemplary foundation for reservoir computing. This positions our discovery at the forefront of utilizing magnetic vortex dynamics for innovative computing solutions, marking a leap towards efficient data processing technologies.
Authors
- A. Hamadeh (ORCID: https://orcid.org/0000-0003-1195-8970)
- Giovanni Finocchio (ORCID: https://orcid.org/0000-0002-1043-3876)
- Vitaliy Lomakin (ORCID: https://orcid.org/0000-0002-5532-4016)
- Philipp Pirro (ORCID: https://orcid.org/0000-0002-0163-8634)
- O. Klein (ORCID: https://orcid.org/0000-0001-9429-5150)
- G. de Loubens (ORCID: https://orcid.org/0000-0001-8096-3058)
- A. Riveros (ORCID: https://orcid.org/0000-0002-4924-663X)
- Davi R. Rodrigues (ORCID: https://orcid.org/0000-0002-6301-4974)
- A. Koujok (ORCID: https://orcid.org/0009-0001-8091-9767)
Institutions
- Universidad de Santiago de Chile (CL)
- Institut polytechnique de Grenoble (FR)
- University of Messina (IT)
- Centre National de la Recherche Scientifique (FR)
- University of Kaiserslautern (DE)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- University of Koblenz and Landau (DE)
- CEA Grenoble (FR)
- University of California San Diego (US)
- Centre de Nanosciences et de Nanotechnologies (FR)
- CEA Paris-Saclay (FR)
- Spintronique et Technologie des Composants (FR)
- Universidad Central de Chile (CL)
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (DE)
- Polytechnic University of Bari (IT)
- Université Grenoble Alpes (FR)
Publication Details
- Journal
- Communications Physics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1038/s42005-025-02006-3
- Citations
- 2
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Deutsche Forschungsgemeinschaft