Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning
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 ferroel...
Gespeichert in:
Veröffentlicht in: | Applied physics letters 2021-09, Vol.119 (13) |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
ISSN: | 0003-6951 1077-3118 |
DOI: | 10.1063/5.0064860 |