Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications - De, Sourav; Baig, Md. Aftab; Qiu, Bo-Han; Müller, Franz; Le, Hoang-Hiep; Lederer, Maximilian; Kämpfe, Thomas; Ali, Tarek; Sung, Po-Jung; Su, Chun-Jung; Lee, Yao-Jen; Lu, Darsen D. (Frontiers in Nanotechnology, 2022)
This work presents 2 bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with 1µs write pulse of maximum ±5V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on SOI wafer using a gate first process, with gate lengths down to 70 nm and fin width 20 nm. Extrapolated retention above ten years also ensures stable inference operation for ten years without any need for re-training. Statistical modeling of device-to-device and cycle-to-cycle variation is performed based on measured data and applied to neural network simulations using the CIMulator software platform. Stochastic device-to-device variation is mainly compensated during online training and has virtually no impact on training accuracy. On the other hand, stochastic cycle-to-cycle threshold voltage variation up to 400mV can be tolerated for MNIST handwritten digits recognition. Substantial inference accuracy drop with systematic retention degradation was observed in analog neural networks. However, quaternary neural networks (QNN) and binary neural networks (BNN) with Fe-finFETs as synaptic devices demonstrated excellent immunity towards the cumulative impact of stochastic and systematic variations.