Abstract
AbstractEmergent 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.
Highlights
There is, a wide range of many-body systems in which interactions vary but reliable, whole-ensembleMagnetic domain walls (DWs) in ferromagnetic nanowires behavior is seen
We found that emergent behavior in the nanoring arrays was observed with similar character in arrays of different width wires (2 and 4 μm) of different thickness (5 and 30 nm), these differences affected the magnetic fields at which the minimum in DW population or magnetization appeared
We have demonstrated that the stochastic nature of magnetic domain wall (DW) propagation in arrays of interconnected soft
Summary
There is, a wide range of many-body systems in which interactions vary but reliable, whole-ensemble. When combined with stochastic DW pinning processes at wire junctions this creates mechanisms of increasing or decreasing DW population, such that interactions of DWs across the array create emergent, non-linear variations of the array magnetization and DW population with the rotating field strength This robust, highly non-linear response of the arrays to external stimuli and “fading memory” of previous magnetization states offers the primary properties required to realize a hardware platform for reservoir computing (RC), a form of neuromorphic computing ideal for analyzing complex transient data series.[27,28,29] To illustrate this, we use a phenomenological model of the magnetic ring arrays’ behavior to perform RC classification of spoken digits
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