Joglekar Shreyas S, Baumgaertl Korbinian, Mucchietto Andrea, Berger Francis, Grundler Dirk
Laboratory of Nanoscale Magnetic Materials and Magnonics, Institute of Materials (IMX), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Institute of Electrical and Micro Engineering (IEM), EPFL, 1015 Lausanne, Switzerland.
Nanoscale Horiz. 2024 Sep 23;9(10):1740-1748. doi: 10.1039/d4nh00095a.
Spin waves (magnons) can enable neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. Encoding magnon signal by reversing magnetization of a nanomagnetic memory bit is pivotal to realize such novel computing schemes efficiently. A magnonic neural network was recently proposed consisting of differently configured nanomagnets that control nonlinear magnon interference in an underlying yttrium iron garnet (YIG) film [Papp , , 2021, , 6422]. In this study, we explore the nonvolatile encoding of magnon signals by switching the magnetization of periodic and aperiodic arrays (gratings) of NiFe (Py) nanostripes with widths between 50 nm and 200 nm. Integrating 50-nm-wide nanostripes with a coplanar waveguide, we excited magnons having a wavelength of ≈100 nm. At a small spin-precessional power of 11 nW, these ultrashort magnons switch the magnetization of 50-nm-wide Py nanostripes after they have propagated over 25 μm in YIG in an applied field. We also demonstrate the magnetization reversal of nanostripes patterned in an aperiodic sequence. We thereby show that the magnon-induced reversal happens regardless of the width and periodicity of the nanostripe gratings. Our study enlarges substantially the parameter regime for magnon-induced nanomagnet reversal on YIG and is important for realizing in-memory computing paradigms making use of magnons with ultrashort wavelengths at low power consumption.
自旋波（磁振子）可实现神经形态计算，旨在克服传统电子学和冯·诺依曼架构固有的局限性。通过反转纳米磁体存储位的磁化方向来编码磁振子信号，对于高效实现此类新型计算方案至关重要。最近有人提出了一种磁振子神经网络，它由不同配置的纳米磁体组成，这些纳米磁体控制着钇铁石榴石（YIG）基底层中的非线性磁振子干涉[帕普，2021年，6422]。在本研究中，我们通过切换宽度在50纳米至200纳米之间的NiFe（Py）纳米条带的周期性和非周期性阵列（光栅）的磁化方向，探索磁振子信号的非易失性编码。将50纳米宽的纳米条带与共面波导集成，我们激发了波长约为100纳米的磁振子。在11纳瓦的小自旋进动功率下，这些超短磁振子在YIG中于外加磁场中传播25微米后，会切换50纳米宽的Py纳米条带的磁化方向。我们还展示了以非周期性序列图案化的纳米条带的磁化反转。由此我们表明，磁振子诱导的反转与纳米条带光栅的宽度和周期性无关。我们的研究极大地扩展了YIG上磁振子诱导纳米磁体反转的参数范围，对于实现利用低功耗超短波长磁振子的内存计算范式具有重要意义。
