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