  The current work reports an efficient deep neural network (DNN) accelerator
where synaptic weight elements are controlled by ferroelectric domain dynamics.
An integrated device-to-algorithm framework for benchmarking novel synaptic
devices is used. In P(VDF-TrFE) based ferroelectric tunnel junctions, analog
conductance states are measured using a custom pulsing protocol and associated
custom circuits and array architectures for DNN training is simulated. Our
results show precise control of polarization switching dynamics in
multi-domain, polycrystalline ferroelectric thin films can produce considerable
weight update linearity in metal-ferroelectric-semiconductor (MFS) tunnel
junctions. Ultrafast switching and low junction current in these devices offer
extremely energy efficient operation. Through an integrated platform of
hardware development, characterization and modelling, we predict the available
conductance range where linearity is expected under identical potentiating and
depressing pulses for efficient DNN training and inference tasks. As an
example, an analog crossbar based DNN accelerator with MFS junctions as
synaptic weight elements showed ~ 93% training accuracy on large MNIST
handwritten digit dataset while for cropped images, a 95% accuracy is achieved.
One observed challenge is rather limited dynamic conductance range while
operating under identical potentiating and depressing pulses below 1V.
Investigation is underway for improving the dynamic conductance range without
losing the weight update linearity.
