A compact skyrmionic leaky–integrate–fire spiking neuron device. Issue 13 (20th March 2018)

• Record Type: Journal Article
• Authors: Chen, Xing; Kang, Wang; Zhu, Daoqian; Zhang, Xichao; Lei, Na; Zhang, Youguang; Zhou, Yan; Zhao, Weisheng
• Is Part Of: Nanoscale. Volume 10:Issue 13(2018)
• Page Start: 6139
• Page End: 6146
• Publication Date: 2018-03-20
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/c7nr09722k
• Languages: English
• ISSNs: 2040-3364

Abstract

Abstract : This study presents a novel compact neuron device that exploits the current-driven skyrmion dynamics in a wedge-shaped nanowire. Abstract : 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.