Robust Binary Neural Network Operation from 233 K to 398 K via Gate Stack and Bias Optimization of Ferroelectric FinFET Synapses

Sourav De, Hoang Hiep Le, Bo Han Qiu, Md Aftab Baig, Po Jung Sung, Chun Jung Su, Yao Jen Lee, Darsen D. Lu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of an Fe-FinFET-based artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the '0' state programmed deep in the subthreshold and the '1' state in strong inversion, is crucial for robust deep neural network inference. The performance of a purely software-based binary neural network (BNN), with 96.1% accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects, and maintained a minimum inference accuracy of 95.2% within a temperature range of -40 to 125 °C after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 125 °C.

Original languageEnglish
Article number9455832
Pages (from-to)1144-1147
Number of pages4
JournalIeee Electron Device Letters
Volume42
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Ferroelectric memory
  • FinFET
  • hafnium
  • hafnium zirconium oxide
  • neural network
  • neuromorphic
  • temperature variation

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