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Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

Authors :
Kai Ni
A. N. M. Nafiul Islam
Arnob Saha
Shan Deng
Zijian Zhao
Abhronil Sengupta
Source :
Applied Physics Letters. 119:133701
Publication Year :
2021
Publisher :
AIP Publishing, 2021.

Abstract

Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies are imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. We provide experimental measurements of the synaptic characteristics for a 28 nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies, and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data.

Details

ISSN :
10773118 and 00036951
Volume :
119
Database :
OpenAIRE
Journal :
Applied Physics Letters
Accession number :
edsair.doi.dedup.....e584c59934a00cbf5e2dae9fc5095b59
Full Text :
https://doi.org/10.1063/5.0064860