Baumgärtl, KorbinianGrundler, Dirk2023-03-292023-03-292023-03-292023-03-2910.1038/s41467-023-37078-8https://infoscience.epfl.ch/handle/20.500.14299/196625Despite 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 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.Magnetic devicesMagnetic storageMemorySpin waveMagnonicsFerrimagnetIn-memory computationGrating couplerMicrowavestext::journal::journal article::research article