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Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour.

Jia-Hui Yuan1, Ya-Bo Chen2, Shu-Qing Dou1

  • 1Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.

Nanotechnology
|December 24, 2021
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Summary
This summary is machine-generated.

This study introduces a novel, low-energy voltage-driven method for stochastic magnetization switching in nanomagnets, enabling energy-efficient artificial neurons. The proposed spintronic neuron demonstrates robustness against fabrication variations, paving the way for reliable artificial neural networks.

Keywords:
nanomagnetsreliabilitysigmoid-like functionspintronic neuronstochastic magnetization switching

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Area of Science:

  • Spintronics
  • Artificial Intelligence
  • Materials Science

Background:

  • Stochastic magnetization switching in nanomagnets is crucial for energy-efficient artificial neurons.
  • Existing methods often require significant energy dissipation.
  • Purely voltage-driven approaches offer a promising path for reduced power consumption.

Purpose of the Study:

  • To propose and investigate a novel, pure voltage-driven scheme for stochastic magnetization switching.
  • To demonstrate the potential of this scheme for creating energy-efficient artificial neurons.
  • To assess the reliability and tolerance to fabrication errors of the designed spintronic neuron.

Main Methods:

  • Utilizing a multiferroic nanomagnet with a pair of electrodes for voltage control.
  • Analyzing magnetization switching probability as a function of voltage pulse width and magnitude.
  • Evaluating the impact of size variations (thickness, width) on neuron performance using the MNIST dataset.

Main Results:

  • A pure voltage-driven scheme with approximately 27.66 aJ energy dissipation was achieved.
  • Magnetization switching probability exhibits a sigmoid-like relationship with voltage pulse parameters, suitable for neuron activation functions.
  • The designed spintronic neuron demonstrated tolerance to reasonable size variations, maintaining high recognition accuracy on the MNIST dataset.

Conclusions:

  • The proposed voltage-driven scheme offers a low-power and reliable method for stochastic magnetization switching.
  • This approach enables the development of energy-efficient and robust artificial neurons for artificial neural networks.
  • The findings open new avenues for low-power, high-reliability spintronic neuromorphic computing.