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Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning
- 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.
- Subjects :
- FOS: Computer and information sciences
Quantitative Biology::Neurons and Cognition
Physics and Astronomy (miscellaneous)
Edge device
Computer Science - Emerging Technologies
Ferroelectricity
Emerging Technologies (cs.ET)
Neuromorphic engineering
Electronic engineering
Performance prediction
Field-effect transistor
Metal gate
Realization (systems)
Voltage
Subjects
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