Diode Characteristics in Magnetic Domain Wall Devices via Geometrical Pinning for Neuromorphic Computing

Neuromorphic computing (NC) is considered a potential vehicle for implementing energy-efficient artificial intelligence. To realize NC, several technologies are being investigated. Among them, the spin-orbit torque (SOT) -driven domain wall (DW) devices are one of the potential candidates. Researchers have proposed different device designs to achieve neurons and synapses, the building blocks of NC. However, the experimental realization of DW device-based NC is only at the primeval stage. Here, we have studied pine-tree DW devices, based on the Laplace pressure on the elastic DWs, for achieving synaptic functionalities and diode-like characteristics. We demonstrate an asymmetric pinning strength for DW motion in two opposite directions to show the potential of these devices as DW diodes. We have used micromagnetic simulations to understand the experimental findings and to estimate the Laplace pressure for various design parameters. The study provides a strategy to fabricate a multifunctional DW device, exhibiting synaptic properties and diode characteristics.

Automated summary

Neuromorphic computing (NC) is a potential approach for energy-efficient artificial intelligence. One of the candidates for NC is the spin-orbit torque-driven domain wall (DW) devices. However, the experimental realization of DW-based NC is at an early stage. In this study, pine-tree DW devices based on Laplace pressure on elastic DWs are investigated for achieving synaptic functionalities and diode-like characteristics. Micromagnetic simulations are used to understand the experimental findings and estimate the Laplace pressure for different design parameters. The study provides a strategy for fabricating multifunctional DW devices with synaptic properties and diode characteristics.