Dynamically Driven Emergence in a Nanomagnetic System

Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers. Arrays of interconnected soft ferromagnetic nanorings contain mobile domain walls (DWs) that are driven around the rings by a rotating magnetic field. DWs have a probability of passing ring junctions, which leads to DW loss/gain mechanisms and, over the array, a stable DW equilibrium population. This emergent property is used to perform reservoir computing on a voice recognition task.