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自归一化短自旋波的深度非线性激发

Deeply nonlinear excitation of self-normalized short spin waves.

作者信息

Wang Qi, Verba Roman, Heinz Björn, Schneider Michael, Wojewoda Ondřej, Davídková Kristýna, Levchenko Khrystyna, Dubs Carsten, Mauser Norbert J, Urbánek Michal, Pirro Philipp, Chumak Andrii V

机构信息

School of Physics, Huazhong University of Science and Technology, Wuhan, China.

Faculty of Physics, University of Vienna, Vienna, Austria.

出版信息

Sci Adv. 2023 Aug 11;9(32):eadg4609. doi: 10.1126/sciadv.adg4609.

DOI:10.1126/sciadv.adg4609
PMID:37566658
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10426902/
Abstract

Spin waves are ideal candidates for wave-based computing, but the construction of magnetic circuits is blocked by a lack of an efficient mechanism to excite long-running exchange spin waves with normalized amplitudes. Here, we solve the challenge by exploiting a deeply nonlinear phenomenon for forward volume spin waves in 200-nm-wide nanoscale waveguides and validate our concept using microfocused Brillouin light scattering spectroscopy. An unprecedented nonlinear frequency shift of more than 2 GHz is achieved, corresponding to a magnetization precession angle of 55° and enabling the excitation of spin waves with wavelengths down to 200 nm. The amplitude of the excited spin waves is constant and independent of the input microwave power due to the self-locking nonlinear shift, enabling robust adjustment of the spin-wave amplitudes in future on-chip magnonic integrated circuits.

摘要

自旋波是基于波的计算的理想候选者,但磁路的构建因缺乏一种有效的机制来激发具有归一化振幅的长时间运行的交换自旋波而受阻。在这里,我们通过利用200纳米宽的纳米级波导中正向体自旋波的深度非线性现象来解决这一挑战,并使用微聚焦布里渊光散射光谱验证了我们的概念。实现了超过2吉赫兹的前所未有的非线性频率偏移,对应于55°的磁化进动角,并能够激发波长低至200纳米的自旋波。由于自锁非线性偏移,激发的自旋波的振幅是恒定的,并且与输入微波功率无关,这使得未来片上磁子集成电路中的自旋波振幅能够进行稳健的调整。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/9ac2c70ab5db/sciadv.adg4609-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/6251965aabfe/sciadv.adg4609-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/e4c174a9c1f8/sciadv.adg4609-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/36f827ee7a12/sciadv.adg4609-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/db064a54aecf/sciadv.adg4609-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/9ac2c70ab5db/sciadv.adg4609-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/6251965aabfe/sciadv.adg4609-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/e4c174a9c1f8/sciadv.adg4609-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/36f827ee7a12/sciadv.adg4609-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/db064a54aecf/sciadv.adg4609-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a537/10426902/9ac2c70ab5db/sciadv.adg4609-f5.jpg

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Nanoscale neural network using non-linear spin-wave interference.利用非线性自旋波干涉的纳米级神经网络。
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