Tunable stochasticity in an artificial spin network

TL;DR

This paper demonstrates how magnetic domain-wall motion in artificial spin networks can be tuned to produce controllable stochastic responses, enabling new computational architectures like Bayesian sensing and random neural networks.

Contribution

It introduces a method to control stochasticity in artificial spin networks via external magnetic fields and lattice modifications, a novel approach for metamaterials.

Findings

Magnetic domain-wall motion causes tunable stochastic responses.

External magnetic fields and lattice modifications control randomness.

Potential applications in post-Von Neumann computing architectures.

Abstract

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. This type of tunable stochastic network opens new paths towards post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.