TY - JOUR
T1 - Controlling the Skyrmion Density and Size for Quantized Convolutional Neural Network
AU - Lone, Aijaz H.
AU - Ganguly, Arnab
AU - Li, Hanrui
AU - El-Atab, Nazek
AU - Setti, Gianluca
AU - Das, Gobind
AU - Fariborzi, H.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Magnetic Tunnel Junction
KW - Magneto-Optical Kerr Effect
KW - Micromagnetics
KW - Neuromorphic devices
KW - Skyrmions
KW - Topological Resistance
UR - http://www.scopus.com/inward/record.url?scp=85207722521&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3472114
DO - 10.1109/ACCESS.2024.3472114
M3 - Article
AN - SCOPUS:85207722521
SN - 2169-3536
JO - IEEE Access
JF - IEEE Access
ER -