Harvesting the Elastic Nature of Domain Walls to Emulate the Synaptic Functions for Brain‐Inspired Computing

Abstract Neuromorphic computing (NC), based on a network of numerous neurons and synapses, promises to lead the future of artificial intelligence (AI). Among various material platforms, spintronic domain wall (DW) devices emerge as promising energy‐efficient candidates for NC. Despite various proposals, the experimental realization of DW devices‐based NC is only starting. Therefore, the concept of ladder DW devices, whose 2D layout resembles a conventional ladder, as a synaptic element, is experimentally studied. First, spin‐orbit torque‐driven DW dynamics is investigated in proposed devices and found DW pinning across a broad range of characteristic parameters (tilt angle ( θ )) and current density. Moreover, maximum pinning strength is observed for θ = 30° in the investigated range. Further, micromagnetic simulations suggest that i) Laplace pressure on the DWs and ii) non‐uniform distribution of the current in various device segments mainly account for the experimental observations. Further, the current density is optimized and observed a total of 19 multilevel magnetization states, confirming the stability of the design in all the studied devices.