Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory

TL;DR

This paper demonstrates that spin waves in yttrium iron garnet can reversibly switch ferromagnetic nanostripes at very low power, paving the way for magnonic in-memory computing to overcome the von Neumann bottleneck.

Contribution

It introduces a novel method of reversing ferromagnetic nanostripes using propagating magnons in yttrium iron garnet, enabling nonvolatile magnon memory.

Findings

Spin waves can reverse ferromagnetic nanostripes at nanowatt power levels.

Magnons can store angular momentum after transmission over macroscopic distances.

This approach enables power-efficient, nonvolatile magnetic memory for in-memory computation.

Abstract

Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves and transport angular momenta through magnets. They enable power-efficient computation without charge flow and would solve the conversion problem if spin wave amplitudes could be stored directly in a magnetic memory cell. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate through an underlying spin-wave bus made from yttrium iron garnet. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level of nW. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures.