Magnonic Full Adder Based on 2D Chiral Magnonic Resonators

TL;DR

This paper demonstrates a magnonic full adder using chiral magnonic resonators and machine learning, achieving high accuracy with minimal physical signals through neural network-based output layers.

Contribution

It introduces a novel magnonic full adder design employing micromagnetic simulations and machine learning, reducing physical signal requirements with neural network output layers.

Findings

Linear output layer requires at least three signals per logical output.

Neural network output layer reduces signals to one per logical output.

Achieves near-perfect classification accuracy with preprocessing.

Abstract

We use micromagnetic simulations to demonstrate how machine learning can be applied to arrays of chiral magnonic resonators to build a magnonic full adder. The chiral magnonic resonators have form of nano-sized permalloy disks that nonlinearly scatter spin waves propagating in a YIG waveguide. The spin waves are injected from multiple outputs, and the dynamic stray magnetic field of the scattered spin waves is sampled in multiple locations to form several physical output signals. These signals are weighted and combined, either linearly or nonlinearly, to satisfy the logic output of a full adder. The process is known as training and forms the device's output layer. The full adder's performance is evaluated in terms of robustness to input and output noise for a given number of physical output signals and the form of the output layer. When the output layer is linear, as few as three physical signals per logical output are sufficient. When a multilayer perceptron neural network forms the output layer, the number of required output signals is reduced to one, and a nearly perfect classification accuracy is achieved when appropriate preprocessing and augmentation strategies are used.