Spintronic devices based on antiferromagnetic skyrmion (AFM) motion on the nanotracks have gained significant interest as a key component of neuromorphic data processing systems. AFM skyrmions are favorable over the ferromagnetic skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edges. In this paper, the AFM skyrmion-based neuron device that exhibits the leaky-integrate-fire (LIF) functionality is proposed for the first time. It exploits the current-driven skyrmion dynamics on the shape-configured nanotracks that are linearly decreasing and exponentially decaying. The device structure creates the regions from lower to higher energy states for the AFM skyrmions during its motion from the wider to narrower region. This causes the repulsion force from the nanotrack edges to act on the AFM skyrmion thereby, drifting it in the backward direction in order to minimize the system energy. This provides the leaking functionality to the neuron device without any external stimuli and additional hardware cost. The average velocities during the integration and leaky processes are in the order of 103 and 102 m/sec, respectively for the linearly and exponentially tapered nanotracks. Moreover, the energy of the skyrmion is in the order 10-20 J. Hence, the suggested device opens up the path for the development of high-speed and energy-efficient devices in antiferromagnetic spintronics for neuromorphic computing.
The development of energy-efficient and ultrafast neuromorphic computing based on the dynamics of the ferromagnetic (FM) skyrmion on the nanotrack has attained considerable interest. In this work, FM skyrmion based artificial neuron device is proposed. The perpendicular magnetic anisotropy (PMA) gradient is created on a thin film ferromagnetic (FM) layer by voltage control-PMA effect (VC-PMA). The anisotropy is directly co-related with the strength of 𝑚𝑧 that affects the size of skyrmion meaning that in the region with larger PMA, the skyrmion size is smaller and hence, more energy. However, the skyrmions have the tendency to move in the direction to minimize the energy. Hence, the skyrmion move towards the lower PMA. This behavior of skyrmion on a nanotrack with PMA gradient corresponds to the leaky-integrate-fire (LIF) functionality of the neuron device. Hence, the suggested energy-efficient artificial neuron opens up the path for developing for energy-efficient neuromorphic computing.
An AFM skyrmion based diode is designed using a staircase notch region at the middle of the nanotrack. The notch region induces the change in potential energy and acts as a barrier, thus allowing the unidirectional motion of the skyrmion.
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