  Magnetic skyrmion technology is promising for the next-generation
spintronics-based memory and neuromorphic computing due to their small size,
non-volatility and low depinning current density. However, the Magnus force
originating from the skyrmion Hall effect causes the skyrmion to move along a
curved trajectory, which may lead to the annihilation of the skyrmion in a
nanotrack during current-induced skyrmion motion. Consequently, circuits
utilizing skyrmionic motion need to be designed to limit the impact of the
skyrmion Hall effect. In this work, we propose a design of an artificial
neuron, and a synapse using the bilayer device consisting of two
antiferromagnetically exchange coupled ferromagnetic layers, which achieves
robustness against the skyrmion Hall effect by nullifying the Magnus force.
Using micromagnetic simulations, we show that the bilayer device can work as an
artificial neuron and also as a synapse by modifying its uniaxial anisotropy.
We also demonstrate that our proposed skyrmionic synapse has an intrinsic
property of perfectly linear and symmetric weight update, which is highly
desirable for the synapse operation. A spiking neural network implemented using
our proposed synapse and neuron was simulated and showed to achieve 96.23\%
accuracy in performing classification on the MNIST handwritten digit dataset.
