Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing

TL;DR

This paper proposes a magnetic cellular nonlinear network utilizing spin wave bus technology for versatile, reconfigurable image processing tasks, demonstrated through numerical simulations showing various filtering and detection functions.

Contribution

It introduces a novel magnetic nanostructure-based network with spin wave communication, enabling reconfigurable image processing without physical re-wiring.

Findings

Successful simulation of image filtering, erosion, dilation, and edge detection.

Control of inter-cell communication via external magnetic field.

Comparison shows advantages over other nano-architectures in power and density.

Abstract

We describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing. The network consists of magneto-electric cells integrated onto a common ferromagnetic film - spin wave bus. The magneto-electric cell is an artificial two-phase multiferroic structure comprising piezoelectric and ferromagnetic materials. A bit of information is assigned to the cell's magnetic polarization, which can be controlled by the applied voltage. The information exchange among the cells is via the spin waves propagating in the spin wave bus. Each cell changes its state as a combined effect of two: the magneto-electric coupling and the interaction with the spin waves. The distinct feature of the network with spin wave bus is the ability to control the inter-cell communication by an external global parameter - magnetic field. The latter makes possible to realize different image processing functions on the same template without rewiring or reconfiguration. We present the results of numerical simulations illustrating image filtering, erosion, dilation, horizontal and vertical line detection, inversion and edge detection accomplished on one template by the proper choice of the strength and direction of the external magnetic field. We also present numerical assets on the major network parameters such as cell density, power dissipation and functional throughput, and compare them with the parameters projected for other nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and Quantum Dot Image Processor. Potentially, the utilization of spin waves phenomena at the nanometer scale may provide a route to low-power consuming and functional logic circuits for special task data processing.