@inproceedings{f05656a0583a460c9db8092a3055d23f,
title = "Voltage Gated Domain Wall Magnetic Tunnel Junction for Neuromorphic Computing Applications",
abstract = "We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion (DWM)-based MTJ device and its application in neuromorphic computing. We show that by utilizing the voltage-controlled gating effect on the DWM, the access transistor can be eliminated. The device provides more control over individual synapse writing and shows highly linear synaptic behavior. The voltage-controlled DW- gating performance of the device in the crossbar array is quantified. The device long-term potentiation/depression (LTP/LTD) linearity dependence on material parameters such as DMI, at different temperatures, is evaluated for real-environment performance analysis. Furthermore, adopting the ideal and skyrmion leaky integrate and fire neuron models, we implement the spiking convolutional neural network for pattern recognition applications The DW-MTJ conductance was mapped to the weights of the SNN. When trained and tested on the CIFAR-10 data set, the architecture based on DW-MTJ synapse achieved accuracy of around 85%.",
keywords = "Domain wall devices, MTJ, Neuromorphic computing, Skyrmions, SNN, Spintronics, Synapses, VCMA",
author = "Lone, {Aijaz H.} and Hanrui Li and Nazek El-Atab and Gianluca Setti and Hossein Fariborzi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Nanotechnology, NANO 2023 ; Conference date: 02-07-2023 Through 05-07-2023",
year = "2023",
doi = "10.1109/NANO58406.2023.10231303",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Nanotechnology",
publisher = "IEEE Computer Society",
pages = "976--981",
booktitle = "2023 IEEE 23rd International Conference on Nanotechnology, NANO 2023",
address = "United States",
}