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

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8661619
Pages (from-to)25-33
Number of pages9
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Volume5
Issue number1
DOIs
StatePublished - Jun 2019

Keywords

  • CMOS
  • Cellular neural network (CNN)
  • Rashba-Edelstein
  • energy efficiency
  • magnetoelectric (ME)
  • spintronics

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