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Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour.
Yuan, Jia-Hui; Chen, Ya-Bo; Dou, Shu-Qing; Wei, Bo; Cui, Huan-Qing; Song, Ming-Xu; Yang, Xiao-Kuo.
Afiliação
  • Yuan JH; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
  • Chen YB; College of Computer, National University of Defense Technology, Changsha 410005, People's Republic of China.
  • Dou SQ; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
  • Wei B; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
  • Cui HQ; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
  • Song MX; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
  • Yang XK; Fundamentals Department, Air Force Engineering University, Xi'an 710051, People's Republic of China.
Nanotechnology ; 33(15)2022 Jan 18.
Article em En | MEDLINE | ID: mdl-34952533
ABSTRACT
Voltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ∼27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it's found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article