Exploring physical and digital architectures in magnetic nanoring array reservoir computers

Abstract

Physical reservoir computing (RC) is a machine learning technique that is ideal for processing of time dependent data series. It is also uniquely well-aligned to in materio computing realisations that allow the inherent memory and non-linear responses of functional materials to be directly exploited for computation. We have previously shown that square arrays of interconnected magnetic nanorings are attractive candidates for in materio RC, and experimentally demonstrated their strong performance in a range of benchmark tasks (Dawidek et al 2021 Adv. Funct. Mater. 31 2008389, Vidamour et al 2022 Nanotechnology 33 485203, Vidamour et al 2023 Commun. Phys. 6 230). Here, we extend these studies to other lattice arrangements of rings, including trigonal and Kagome grids, to explore how these affect both the magnetic behaviours of the arrays, and their computational properties. We show that while lattice geometry substantially affects the microstate behaviour of the arrays, these differences manifest less profoundly when averaging magnetic behaviour across the arrays. Consequently the computational properties (as measured using task agnostic metrics) of devices with a single electrical readout are found to be only subtly different, with the approach used to time-multiplex data into and out of the arrays having a stronger effect on properties than the lattice geometry. However, we also find that hybrid reservoirs that combine the outputs from arrays with different lattice geometries show enhanced computational properties compared to any single array.

Keywords

• reservoir computing
• machine learning
• magnetic domain wall devices