Learning Trajectories from Spin-Wave Dynamics

The efficacy of a physical reservoir computer model based on traveling spin waves in a spin-wave delay-line active-ring resonator was demonstrated recently. In the present work, we investigate how this neuromorphic device can be adapted for sensing applications. In this reservoir computing for sensing framework, we exploit strong coupling of the physical reservoir to its environment to utilize the reservoir as a sensing element. The dynamics of traveling spin waves in delay-line active rings are strongly depen-dent on the magnetic field and carrier frequency of those spin waves. Treating the spin-wave frequency as an environmental variable, we excite the active ring into different dynamical states by modulating the carrier frequency of a drive signal of microwave pulses injected into the ring. Training a linear regression on the time-multiplexed output from the ring allows the periodic amplitude patterns of the spin waves to be mapped reproducibly onto two-dimensional trajectories, representing periodic behavioral targets. Our work demonstrates the versatility of a magnonic resonator as a multipurpose computing and sensing device.

Automated summary

The effectiveness of a physical reservoir computer model based on traveling spin waves in a spin-wave delay-line active-ring resonator has recently been demonstrated. This study explores the adaptation of this neuromorphic device for sensing applications. By taking advantage of the strong coupling between the physical reservoir and its environment, the reservoir is used as a sensing element in the reservoir computing for sensing framework. The dynamics of traveling spin waves in the delay-line active rings depend heavily on the magnetic field and carrier frequency of these spin waves.