  This paper reports a comprehensive study on the impacts of
temperature-change, process variation, flicker noise and device aging on the
inference accuracy of pre-trained all-ferroelectric (FE) FinFET deep neural
networks. Multiple-level-cell (MLC) operation with a novel
adaptive-program-and-read algorithm with 100ns write pulse has been
experimentally demonstrated in 5 nm thick hafnium zirconium oxide (HZO)-based
FE-FinFET. With pre-trained neural network (NN) with 97.5% inference accuracy
on MNIST dataset as baseline, device to device variation is shown to have
negligible impact. Flicker noise characterization at various bias conditions
depicts that drain current fluctuation is less than 0.7% with virtually no
inference accuracy degradation. The conductance drift of a programmed cell, as
an aftermath of temperature change, was captured by a compact model over a wide
range of gate biases. Despite significant inference accuracy degradation at
233K for a NN trained at 300K, gate bias optimization for recovering the
accuracy is demonstrated. Endurance above 10$^8$ cycles and extrapolated
retention above 10 years are shown, which paves the way for edge device
artificial intelligence with FE-FinFETs.
