Parallel synaptic design of ferroelectric tunnel junctions for neuromorphic computing

Abstract We propose a novel synaptic design of more efficient neuromorphic edge-computing with substantially improved linearity and extremely low variability. Specifically, a parallel arrangement of ferroelectric tunnel junctions (FTJ) with an incremental pulsing scheme provides a great improvement...

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Bibliographic Details
Published in:Neuromorphic computing and engineering 2023-06, Vol.3 (2), p.24001
Main Authors: Moon, Taehwan, Lee, Hyun Jae, Nam, Seunggeol, Bae, Hagyoul, Choe, Duk-Hyun, Jo, Sanghyun, Lee, Yun Seong, Park, Yoonsang, Yang, J Joshua, Heo, Jinseong
Format: Article
Language:English
Subjects:
artificial synapse
ferroelectric tunnel junction
hafnium oxide
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Summary:Abstract We propose a novel synaptic design of more efficient neuromorphic edge-computing with substantially improved linearity and extremely low variability. Specifically, a parallel arrangement of ferroelectric tunnel junctions (FTJ) with an incremental pulsing scheme provides a great improvement in linearity for synaptic weight updating by averaging weight update rates of multiple devices. To enable such design with FTJ building blocks, we have demonstrated the lowest reported variability: σ / μ = 0.036 for cycle to cycle and σ / μ = 0.032 for device among six dies across an 8 inch wafer. With such devices, we further show improved synaptic performance and pattern recognition accuracy through experiments combined with simulations.
ISSN:2634-4386
2634-4386
DOI:10.1088/2634-4386/accc51