Controlling the Skyrmion Density and Size for Quantized Convolutional Neural Network

The exceptional properties of skyrmion devices, including their miniature size, topologically protected nature, and low current requirements, render them highly promising for energy-efficient neuromorphic computing applications. Examining the creation, stability, and dynamics of magnetic skyrmions in thin-film systems is imperative to realize these skyrmion-based neuromorphic devices. Herein, we report the creation, stability, and tunability of magnetic skyrmions in the Ta/IrMn/CoFeB/MgO thin-film system. We use polar magneto-optic Kerr effect (MOKE) microscopy and micromagnetic simulations to investigate the magnetic-field dependence of skyrmion density and size. The topological charge evolution with time under a magnetic field is studied, and the transformation dynamics are explained. Furthermore, we demonstrate skyrmion size and density tunability as parameters controlled by voltage, current, and magnetic field via Voltage-Controlled Magnetoresistance (VCMA) and Dzyaloshinskii-Moriya Interaction (DMI). We propose a skyrmion-based synaptic device for neuromorphic computing applications. The device exhibits spin-orbit torque-controlled discrete topological resistance states with high linearity and uniformity, allowing for the realization of the hardware implementation of weight quantization in a Quantized Convolutional Neural Network (QCNN). Our experimental results demonstrate that the devices can be trained and tested on the CIFAR-10 dataset, achieving a recognition accuracy of ~87%. The findings open new avenues for developing neuromorphic computing devices based on tunable skyrmion systems.