  Magnetic skyrmion-based data storage and unconventional computing devices
have gained increasing attention due to their topological protection, small
size, and low driving current. However, skyrmion creation, deletion, and motion
are still being studied. In this study, we propose a skyrmion-based
neuromorphic magnetic tunnel junction (MTJ) device with both long- and
short-term plasticity (LTP and STP) (mixed synaptic plasticity). We showed that
plasticity could be controlled by magnetic field, spin-orbit torque (SOT), and
the voltage-controlled magnetic anisotropy (VCMA) switching mechanism. LTP
depends on the skyrmion density and is manipulated by the SOT and magnetic
field while STP is controlled by the VCMA. The LTP property of the device was
utilized for static image recognition. By incorporating the STP feature, the
device gained additional temporal filtering ability and could adapt to a
dynamic environment. The skyrmions were conserved and confined to a nanotrack
to minimize the skyrmion nucleation energy. The synapse device was trained and
tested for emulating a deep neural network. We observed that when the skyrmion
density was increased, the inference accuracy improved: 90% accuracy was
achieved by the system at the highest density. We further demonstrated the
dynamic environment learning and inference capabilities of the proposed device.
