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超低电流10纳米自旋霍尔纳米振荡器

Ultra-Low Current 10 nm Spin Hall Nano-Oscillators.

作者信息

Behera Nilamani, Chaurasiya Avinash Kumar, González Victor H, Litvinenko Artem, Bainsla Lakhan, Kumar Akash, Khymyn Roman, Awad Ahmad A, Fulara Himanshu, Åkerman Johan

机构信息

Physics Department, University of Gothenburg, Gothenburg, 412 96, Sweden.

Department of Physics, Indian Institute of Technology Ropar, Roopnagar, 140001, India.

出版信息

Adv Mater. 2024 Feb;36(5):e2305002. doi: 10.1002/adma.202305002. Epub 2023 Dec 5.

Abstract

Nano-constriction based spin Hall nano-oscillators (SHNOs) are at the forefront of spintronics research for emerging technological applications, such as oscillator-based neuromorphic computing and Ising Machines. However, their miniaturization to the sub-50 nm width regime results in poor scaling of the threshold current. Here, it shows that current shunting through the Si substrate is the origin of this problem and studies how different seed layers can mitigate it. It finds that an ultra-thin Al O seed layer and SiN (200 nm) coated p-Si substrates provide the best improvement, enabling us to scale down the SHNO width to a truly nanoscopic dimension of 10 nm, operating at threshold currents below 30 A. In addition, the combination of electrical insulation and high thermal conductivity of the Al O seed will offer the best conditions for large SHNO arrays, avoiding any significant temperature gradients within the array. The state-of-the-art ultra-low operational current SHNOs hence pave an energy-efficient route to scale oscillator-based computing to large dynamical neural networks of linear chains or 2D arrays.

摘要

基于纳米缩颈的自旋霍尔纳米振荡器(SHNO)处于自旋电子学研究的前沿,用于新兴技术应用,如基于振荡器的神经形态计算和伊辛机。然而,将其缩小到50纳米以下宽度的范围会导致阈值电流的缩放效果不佳。在此,研究表明电流通过硅衬底分流是该问题的根源,并研究了不同的种子层如何缓解这一问题。研究发现,超薄的AlO种子层和涂有SiN(200纳米)的p型硅衬底能提供最佳的改善效果,使我们能够将SHNO的宽度缩小到真正的纳米尺寸10纳米,在低于30μA的阈值电流下工作。此外,AlO种子层的电绝缘性和高导热性的结合将为大型SHNO阵列提供最佳条件,避免阵列内出现任何显著的温度梯度。因此,最先进的超低工作电流SHNO为将基于振荡器的计算扩展到线性链或二维阵列的大型动态神经网络铺平了一条节能途径。

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