Neuromorphic Computing with Ferroelectric FinFETs in the Presence of Temperature, Process Variation, Device Aging and Flicker Noise. (arXiv:2103.13302v2 [cs.ET] UPDATED)

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 …

aging arxiv computing neuromorphic neuromorphic computing noise process