  This paper reports a comprehensive study on the applicability of ultra-scaled
ferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer for
neuromorphic computing in the presence of process variation, flicker noise, and
device aging. An intricate study has been conducted about the impact of such
variations on the inference accuracy of pre-trained neural networks consisting
of analog, quaternary (2-bit/cell) and binary synapse. A pre-trained neural
network with 97.5% inference accuracy on the MNIST dataset has been adopted as
the baseline. Process variation, flicker noise, and device aging
characterization have been performed and a statistical model has been developed
to capture all these effects during neural network simulation. Extrapolated
retention above 10 years have been achieved for binary read-out procedure. We
have demonstrated that the impact of (1) retention degradation due to the oxide
thickness scaling, (2) process variation, and (3) flicker noise can be abated
in ferroelectric FinFET based binary neural networks, which exhibits superior
performance over quaternary and analog neural network, amidst all variations.
The performance of a neural network is the result of coalesced performance of
device, architecture and algorithm. This research corroborates the
applicability of deeply scaled ferroelectric FinFETs for non-von Neumann
computing with proper combination of architecture and algorithm.
