Reprogrammable magnonic logic in a multiferroic heterostructure via magnetoelectric coupling

TL;DR

This paper demonstrates a non-volatile, voltage-controlled reprogrammable magnonic device using a multiferroic heterostructure, enabling scalable, energy-efficient on-chip magnonic logic and advanced signal processing functions.

Contribution

It introduces a novel approach to electrically reconfigure magnonic responses via ferroelectric domain engineering in a multiferroic heterostructure, enabling scalable, non-volatile magnonic devices.

Findings

Frequency shifts up to ~150 MHz achieved.

Electrically defined, spatially programmable magnonic waveguides demonstrated.

Platform capable of advanced functions like frequency demultiplexing.

Abstract

The realization of fully reconfigurable, voltage-controlled, and programmable on-chip magnonic devices is essential to fully harness the potential of spin waves for signal processing, logic and neuromorphic computing. Yet, existing demonstrations of electrical tuning of magnonic responses are either volatile, current-driven and thus energy-inefficient, or rely on local strain modification limiting their scalability for wafer-scale integration. Here, we address this challenge using a BiFeO3/La0.67Sr0.33MnO3 multiferroic thin film heterostructure. We show that ferroelectric domain engineering in BiFeO3 enables deterministic tuning of the magnon dispersion of La0.67Sr0.33MnO3, producing frequency shifts up to $\sim 150 MHz$ and allowing reconfigurable waveguiding. Micro-focused Brillouin light scattering directly images these effects, revealing electrically defined magnonic waveguides and spatially programmable dispersion. Compared to conventional approaches, this method provides non-volatile and reversible control. Furthermore, using an inverse-design simulation code, we demonstrate the capability of our platform to perform advanced magnonic functions such as frequency demultiplexing. Our results open a new avenue for using magnetoelectric heterostructures for magnonic logic, with further applicability to reservoir and neuromorphic computing and AI driven magnonic devices.