Jump to main content
Jump to site search

Issue 13, 2018
Previous Article Next Article

A compact skyrmionic leaky–integrate–fire spiking neuron device

Author affiliations


Neuromorphic computing, which relies on a combination of a large number of neurons massively interconnected by an even larger number of synapses, has been actively studied for its characteristics such as energy efficiency, intelligence, and adaptability. To date, while the development of artificial synapses has shown great progress with the introduction of emerging nanoelectronic devices, e.g., memristive devices, the implementation of artificial neurons, however, depends mostly on semiconductor-based circuits via integrating many transistors, sacrificing energy efficiency and integration density. Here, we present a novel compact neuron device that exploits the current-driven magnetic skyrmion dynamics in a wedge-shaped nanotrack. Under the coaction of the exciting current pulse and the repulsive force exerted by the nanotrack edges, the dynamic behavior of the proposed skyrmionic artificial neuron device is in analogy to the leaky–integrate–fire (LIF) spiking function of a biological neuron. The tunable temporary location of the skyrmion in our artificial neuron behaves like the analog membrane potential of a biological neuron. The neuronal dynamics and the related physical interpretations of the proposed skyrmionic neuron device are carefully investigated via micromagnetic and theoretical methods. Such a compact artificial neuron enables energy-efficient and high-density implementation of neuromorphic computing hardware.

Graphical abstract: A compact skyrmionic leaky–integrate–fire spiking neuron device

Back to tab navigation

Supplementary files

Publication details

The article was received on 31 Dec 2017, accepted on 21 Feb 2018 and first published on 21 Feb 2018

Article type: Paper
DOI: 10.1039/C7NR09722K
Citation: Nanoscale, 2018,10, 6139-6146
  •   Request permissions

    A compact skyrmionic leaky–integrate–fire spiking neuron device

    X. Chen, W. Kang, D. Zhu, X. Zhang, N. Lei, Y. Zhang, Y. Zhou and W. Zhao, Nanoscale, 2018, 10, 6139
    DOI: 10.1039/C7NR09722K

Search articles by author