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Spintronics is an important emerging technology for data storage andcomputation. In this field, magnetic skyrmion-based devices are attractive dueto their small size and energy consumption. However, controlling the creation,deletion and motion of skyrmions is challenging. Here we propose a novelenergy-efficient skyrmion-based device structure, and demonstrate its use asleaky integrate (LIF) and fire neuron for neuromorphic computing. Here we showthat skyrmions can be confined by patterning the geometry of the free layer ina magnetic tunnel junction (MTJ), and demonstrate that the size of the skyrmioncan be adjusted by applying pulsed voltage stresses. A spiking neural network(SNN) made of such skyrmion-based LIF neurons shows the capability ofclassifying images from the Modified National Institute of Standards andTechnology (MNIST) dataset.

Skyrmion-based Leaky Integrate and Fire Neurons for Neuromorphic Applications