Understanding interfacial spin transport is key to developing magnetoelectonic devices, however, the exact nature of the parameters involved is unclear. Here, we report a detailed ferromagnetic resonance-based spintransport analysis on a variety of structures of both ferromagnetic (Co, CoFeB) and heavy metal layers (Pt, Ru) in order to fully quantify the interfacial spin-transport parameters. Enhanced spin-mixing conductance is observed for more closely matched ferromagnet and heavy metal crystal structures, and, significantly, the inclusion of a thickness-dependent spin-diffusion length gives a bulk value of 9.4 ± 0.7 nm for Pt, resolving reported discrepancies.
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.
The proximity-induced moment (PIM) in heavy metal layers may play a significant role in heterostructured spintronic systems. In particular, the PIM of a heavy metal adjacent to a magnetic layer has been linked to interfacial spin transport behavior. Element-resolved x-ray magnetic measurements were used to investigate PIM in Pt layered with two different rare-earth (RE):3d transition-metal (TM) ferrimagnetic alloys in which the net moment was dominated by either the RE or the TM at room temperature. We observed significant PIM in Pt confined to a 2-nm interfacial region for Pt/Co 77 Gd 23 and Pt/(Fe 50 Co 50) 77 Gd 23 and, in both cases, the PIM was parallel to the TM sublattice rather than the RE or the net moment. Our results highlight the prominence of the d − d mediated interactions between the Pt and the constituents of the ferrimagnetic RE:TM alloys over the net macroscopic moment.
Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings’ computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir’s output to include multiple, concurrent measures of the ring arrays’ magnetic states.
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