Fert Beijing Institute, BDBC, and School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.
Nanoscale. 2018 Mar 29;10(13):6139-6146. doi: 10.1039/C7NR09722K.
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.
神经形态计算依赖于大量神经元通过数量更多的突触进行大规模互连,它具有节能、智能和自适应等特点,目前受到了广泛关注。尽管随着新兴纳米电子器件(如忆阻器)的出现,人工突触的发展取得了很大的进展,但人工神经元的实现主要还是依赖于基于半导体的电路,通过集成大量晶体管来实现,这牺牲了能量效率和集成密度。在这里,我们提出了一种新颖的紧凑神经元器件,利用楔形纳米轨道中的电流驱动磁斯格明子动力学。在激励电流脉冲和纳米轨道边缘的排斥力的共同作用下,所提出的斯格明子人工神经元器件的动态行为类似于生物神经元的漏积分点火(LIF)尖峰功能。我们的人工神经元中斯格明子的可调暂存位置类似于生物神经元的模拟膜电位。通过使用微磁学和理论方法,我们仔细研究了所提出的斯格明子神经元器件的神经元动力学及其相关物理解释。这种紧凑的人工神经元能够实现高能效和高密度的神经形态计算硬件。