Laboratory of Nanoscale Magnetic Materials and Magnonics, Institute of Materials (IMX), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Institute of Electrical and Micro Engineering (IEM), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Nat Commun. 2023 Mar 29;14(1):1490. doi: 10.1038/s41467-023-37078-8.
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 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.
尽管 CMOS 集成电路的规模前所未有地缩小，但内存密集型机器学习和人工智能应用受到了内存和处理器之间数据转换的限制。人们正在寻求新颖的方法来克服这种所谓的冯·诺依曼瓶颈。磁振子是自旋波的量子。它们的角动量使无需电荷流动即可实现高能效计算。如果自旋波的幅度可以直接存储在磁存储器中，那么转换问题就可以得到解决。在这里，我们报告了通过在下面的自旋波总线中传播的自旋波来反转铁磁纳米带。这样，在经过宏观距离传输后，无电荷的角动量流就被存储起来。我们表明，自旋波可以以非常小的功率水平反转大阵列的铁磁条纹。结合已经存在的波逻辑，我们的发现为基于磁振子的内存计算和超越冯·诺依曼计算机体系结构的新时代开辟了道路。
