论文标题
通过在铁磁性Yttrium铁石榴石中传播镁来逆转纳米磁铁
Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory
论文作者
论文摘要
尽管CMOS集成电路的前所未有的缩减，但内存密集型机器学习和人工智能应用程序受到内存和处理器之间的数据转换的限制。对于克服这种所谓的冯·诺伊曼瓶颈的新方法，有一个具有挑战性的追求。镁质是自旋波和通过磁铁传输角动量的量子。它们可以在没有电荷流量的情况下实现发电的计算，如果可以将自旋波振幅直接存储在磁记忆电池中，将解决转换问题。在这里，我们报告了通过旋转波通过由Yttrium Iron Garnet制成的潜在的自旋波线传播的旋转波逆转纳米条的逆转。因此，在宏观距离上传输后，将无电荷的角动量流储存。我们表明，旋转波可以在NW的极小功率水平上扭转大量的铁磁条纹。结合已经存在的波浪逻辑，我们的发现是基于镁质的内存计算和冯·诺伊曼计算机架构的新时代的突破。
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.
