Imaging the emergent behaviour in nanoring assemblies for reservoir computing applications

Abstract

Stochastic behaviour has traditionally been a limiting factor in the development of nanomagnetic technology. However, it can also give rise to rich physical behaviours. For example, in artificial spin ice (ASI) arrays [1,2] stochastic effects give rise to emergent behaviour that can help us to understand phenomena in frustrated systems. In particular, this emergent behaviour is useful for a novel form of neuromorphic computing called ‘reservoir computing’ which is highly efficient for time domain processing of signals [3]. One such system that exhibits probabilistic, emergent behaviour with complexity is an array of interconnected nanowire rings of Ni80Fe20 [4-5]. Here we characterise the emergent behaviour of domain wall (DW) states in a nanoring array using X-ray imaging under the application of rotating fields of different amplitudes in order to better understand the array’s magnetic response and ascertain its suitability for reservoir computing.Fig. 1 (A) shows an SEM micrograph of a representative rectangular Ni80Fe20 ring array of which we have obtained the magnetic response by applying an in-plane rotating magnetic field. Magneto-optic Kerr effect (MOKE) measurements show a strongly nonlinear magnetisation variation with field amplitude (Fig. 1 (B)) - an essential response for reservoir computing. We have shown this system to be a promising candidate for reservoir computing using various macroscopic measurement techniques [5-6].It is clear that these complex dynamics are due to the interplay of pinned and propagating DWs (Fig. 1 (B)) and different magnetic states in the array. We used X-ray Photo Emission Electron Microscopy (X-PEEM) to image the magnetic states across a ring array with rings of radius 2 microns and width of 500 nm with an Ni80Fe20 thickness of 10 nm and with a 2nm Al layer to prevent oxidation. X-PEEM images were obtained with an Elmitec SPELEEM-III microscope on the I06 beamline at the Diamond Light Source using XMCD at the Fe-L3 edge. Samples were mounted on cartridges with a quadrupole magnet, to provide an in-plane magnetic field with arbitrary direction, designed and built at the CIRCE XPEEM beamline at the ALBA Synchrotron. Samples were subject to an initialisation field pulse of 160 Oe followed by 30 rotations of an in-plane magnetic field of various strengths.A representative X-PEEM image of the array (Fig. 2 (A)) obtained with a rotating field strength of 30 Oe at a frequency of 8 Hz shows multiple magnetisation states. The allowed magnetic configurations are shown schematically in Fig. 2 (B). There are four types of ‘onion’ states (two DWs separated by 180°), two ‘vortex’ states (zero DWs) and eight ‘three-quarter’ states (two DWs separated by 90°) possible. A dedicated Python code was used to analyse the magnetisation states in the X-PEEM image (Fig. 2 (A)). The rings were mapped to circles using the Hough transform available in the OpenCV library [7] and the color of each quarter was recorded. Then the color scheme of an entire circle was assigned to a magnetic configuration. The population of various states at different amplitudes of the rotating field was extracted and is shown in Fig. 2 (C). We can see that ‘onion’ states (at lower fields) give way to ‘vortex’ and ‘three-quarter’ states and at higher fields ‘onion’ states dominate again. This is consistent with the previous MOKE measurements that were attributed to static onion states at low field magnitudes, due to DW pinning, and dynamic onion states at high fields (as shown in Fig. 1 (B)) when DWs readily overcome the pinning potentials presented by ring junctions. Note that this behaviour leads to the emergent dynamics which the ring assembly shows under the influence of a rotating magnetic field. The variation in the magnetic states population also indicates ‘fading memory’ as the state population at a given field is independent of the population at a previous field. This is another desirable property for a system to function as a reservoir. The normalised magnetisation response of the array can be extracted from the state population (Fig. 2 (D)) and in order to measure the array’s response to field strength. This is similar to that obtained from previous MOKE measurements but highlights the significance of dynamic local variation in magnetic structure.We have thus imaged the emergent DW dynamics in nanoring arrays using X-PEEM and have obtained their magnetic response to show their suitability for reservoir computing applications. The different complex magnetic states in the array, which give rise to their emergent magnetic behaviour, provide insights into the evolution of these states. We hope that this study will pave the way for further investigations into the suitability and implementation of these systems for advanced computing purposes. **