Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"

<p>Raw data associated to the manuscript &lsquo;&rsquo;Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory&lsquo;&rsquo;, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> 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. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. 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</p>