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Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

Journal Article · · Applied Physics Letters
DOI:https://doi.org/10.1063/5.0064860· OSTI ID:1831803
 [1];  [2];  [3];  [3];  [3];  [2]
  1. Pennsylvania State Univ., University Park, PA (United States); Pennsylvania State University
  2. Pennsylvania State Univ., University Park, PA (United States)
  3. Rochester Inst. of Technology, Rochester, NY (United States)
+ Show Author Affiliations
Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is 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. Here, we provide experimental measurements of the synaptic characteristics for a 28nm 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
Research Organization:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0021118
OSTI ID:
1831803
Alternate ID(s):
OSTI ID: 1823193
Journal Information:
Applied Physics Letters, Journal Name: Applied Physics Letters Journal Issue: 13 Vol. 119; ISSN 0003-6951
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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