  This paper reports the impacts of temperature variation on the inference
accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along
with plausible design techniques to abate these impacts. We adopted a
pre-trained artificial neural network (N.N.) with 96.4% inference accuracy on
the MNIST dataset as the baseline. As an aftermath of temperature change, a
compact model captured the conductance drift of a programmed cell over a wide
range of gate biases. We observed a significant inference accuracy degradation
in the analog neural network at 233 K for an N.N. trained at 300 K. Finally, we
deployed binary neural networks with "read voltage" optimization to ensure
immunity of N.N. to accuracy degradation under temperature variation,
maintaining an inference accuracy of 96%. Keywords: Ferroelectric memories
