Random and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications

Sourav De, Md Aftab Baig, Bo Han Qiu, Franz Müller, Hoang Hiep Le, Maximilian Lederer, Thomas Kämpfe, Tarek Ali, Po Jung Sung, Chun Jung Su, Yao Jen Lee, Darsen D. Lu

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

This work presents 2-bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with a 1 µs write pulse of maximum ±5 V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on an SOI wafer using a gate first process, with gate lengths down to 70 nm and fin width 20 nm. Extrapolated retention above 10 years also ensures stable inference operation for 10 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 400 mV can be tolerated for MNIST handwritten digits recognition. A substantial inference accuracy drop with systematic retention degradation was observed in analog neural networks. However, quaternary neural networks (QNNs) and binary neural networks (BNNs) with Fe-finFETs as synaptic devices demonstrated excellent immunity toward the cumulative impact of stochastic and systematic variations.

原文English
文章編號826232
期刊Frontiers in Nanotechnology
3
DOIs
出版狀態Published - 2022 1月 26

All Science Journal Classification (ASJC) codes

  • 電子、光磁材料
  • 原子與分子物理與光學
  • 生物醫學工程
  • 電腦科學應用
  • 電氣與電子工程

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