  Nanoelectronic devices emulating neuro-synaptic functionalities through their
intrinsic physics at low operating energies is imperative toward the
realization of brain-like neuromorphic computers. In this work, we leverage the
non-linear voltage dependent partial polarization switching of a ferroelectric
field effect transistor to mimic plasticity characteristics of biological
synapses. We provide experimental measurements of the synaptic characteristics
for a $28nm$ high-k metal gate technology based device and develop an
experimentally calibrated device model for large-scale system performance
prediction. Decoupled read-write paths, ultra-low programming energies and the
possibility of arranging such devices in a cross-point architecture demonstrate
the synaptic efficacy of the device. Our hardware-algorithm co-design analysis
reveals that the intrinsic plasticity of the ferroelectric devices has
potential to enable unsupervised local learning in edge devices with limited
training data.
