Reinforcement-Learning-Designed Field-Free Sub-Nanosecond Spin-Orbit-Torque Switching
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918125169016832 |
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| author | Igarashi, Yuta Fujimoto, Junji |
| author_facet | Igarashi, Yuta Fujimoto, Junji |
| contents | We demonstrate deterministic, field-free magnetization reversal of a single-domain nanomagnet within 300 ps under a current density of $3 \times 10^{10}~\mathrm{A/m^2}$ by coupling reinforcement learning (RL) to the Landau-Lifshitz-Gilbert equation with the spin-orbit torques (SOTs). The RL agent autonomously discovers a current waveform that minimizes the magnetization trajectory path and exploits a precessional shortcut enabled by the field-like SOT and hard-axis anisotropy. From the learned pulse, we extract a clear physical picture of the dynamics and develop a model-based analytical framework that establishes a lower bound on the switching time. The control strategy remains robust across a wide range of damping constants and is stabilized against thermal fluctuations at higher current densities. We also discuss feasible experimental implementations for the precessional switching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement-Learning-Designed Field-Free Sub-Nanosecond Spin-Orbit-Torque Switching Igarashi, Yuta Fujimoto, Junji Mesoscale and Nanoscale Physics We demonstrate deterministic, field-free magnetization reversal of a single-domain nanomagnet within 300 ps under a current density of $3 \times 10^{10}~\mathrm{A/m^2}$ by coupling reinforcement learning (RL) to the Landau-Lifshitz-Gilbert equation with the spin-orbit torques (SOTs). The RL agent autonomously discovers a current waveform that minimizes the magnetization trajectory path and exploits a precessional shortcut enabled by the field-like SOT and hard-axis anisotropy. From the learned pulse, we extract a clear physical picture of the dynamics and develop a model-based analytical framework that establishes a lower bound on the switching time. The control strategy remains robust across a wide range of damping constants and is stabilized against thermal fluctuations at higher current densities. We also discuss feasible experimental implementations for the precessional switching. |
| title | Reinforcement-Learning-Designed Field-Free Sub-Nanosecond Spin-Orbit-Torque Switching |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2508.10792 |