Neuromorphic Computing with Fe-FinFETs in the Presence of Variation
Sourav De, Md. Aftab Baig, Bo-Han Qiu, Hoang‐Hiep Le, Yao‐Jen Lee, Darsen D. Lu
Abstract
This paper reports a comprehensive study on the impacts of process variation 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.