Scientific Reports (May 2023)

Proposing magnetoimpedance effect for neuromorphic computing

  • Loghman Jamilpanah,
  • Alessandro Chiolerio,
  • Marco Crepaldi,
  • Andrew Adamatzky,
  • Majid Mohseni

DOI
https://doi.org/10.1038/s41598-023-35876-0
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 7

Abstract

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Abstract Oscillation of physical parameters in materials can result in a peak signal in the frequency spectrum of the voltage measured from the materials. This spectrum and its amplitude/frequency tunability, through the application of bias voltage or current, can be used to perform neuron-like cognitive tasks. Magnetic materials, after achieving broad distribution for data storage applications in classical Von Neumann computer architectures, are under intense investigation for their neuromorphic computing capabilities. A recent successful demonstration regards magnetisation oscillation in magnetic thin films by spin transfer or spin orbit torques accompanied by magnetoresistance (MR) effect that can give a voltage peak in the frequency spectrum of voltage with bias current dependence of both peak frequency and amplitude. Here we use classical magnetoimpedance (MI) effect in a magnetic wire to produce such a peak and manipulate its frequency and amplitude by means of the bias voltage. We applied a noise signal to a magnetic wire with high magnetic permeability and owing to the frequency dependence of the magnetic permeability we got frequency dependent impedance with a peak at the maximum permeability. Frequency dependence of the MI effect results in different changes in the voltage amplitude at each frequency when a bias voltage is applied and therefore a shift in the peak position and amplitude can be obtained. The presented method and material provide optimal features in structural simplicity, low-frequency operation (tens of MHz-order) and high robustness at different environmental conditions. Our universal approach can be applied to any system with frequency dependent bias responses.