Voltage Gated Domain Wall Magnetic Tunnel Junction-based Spiking Convolutional Neural Network

Aijaz H Lone; Hanrui Li; Nazek El-Atab; Xiaohang Li; Hossein Fariborzi

Voltage Gated Domain Wall Magnetic Tunnel Junction-based Spiking Convolutional Neural Network

Applied Physics 2022-12-20 v1 Emerging Technologies

Authors: Aijaz H Lone , Hanrui Li , Nazek El-Atab , Xiaohang Li , Hossein Fariborzi

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 linearity dependence on material parameters such as DMI and temperature is evaluated for real-environment performance analysis. Furthermore, using skyrmion-based leaky integrate and fire neuron model, we implement the spiking convolutional neural network for pattern recognition applications on the CIFAR-10 data set. The accuracy of the device is above 85%, proving its applicability in SNN.

Keywords

neuromorphic computing field-effect transistors

Cite

@article{arxiv.2212.09444,
  title  = {Voltage Gated Domain Wall Magnetic Tunnel Junction-based Spiking Convolutional Neural Network},
  author = {Aijaz H Lone and Hanrui Li and Nazek El-Atab and Xiaohang Li and Hossein Fariborzi},
  journal= {arXiv preprint arXiv:2212.09444},
  year   = {2022}
}

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6 pages and 5 figures

Voltage Gated Domain Wall Magnetic Tunnel Junction-based Spiking Convolutional Neural Network

Abstract

Keywords

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