Performance Estimate of Inverse Rashba&#x2013;Edelstein Magnetoelectric Devices for Neuromorphic Computing

Andrew W. Stephan; Jiaxi Hu; Steven J. Koester

doi:10.1109/JXCDC.2019.2903286

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (Jan 2019)

Performance Estimate of Inverse Rashba–Edelstein Magnetoelectric Devices for Neuromorphic Computing

Andrew W. Stephan,
Jiaxi Hu,
Steven J. Koester

Affiliations

Andrew W. Stephan: ORCiD; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
Jiaxi Hu: ORCiD; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
Steven J. Koester: ORCiD; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA

DOI: https://doi.org/10.1109/JXCDC.2019.2903286
Journal volume & issue: Vol. 5, no. 1
pp. 25 – 33

Abstract

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We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.

Published in IEEE Journal on Exploratory Solid-State Computational Devices and Circuits

ISSN: 2329-9231 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6570653

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