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用作模拟和二进制随机神经元的低势垒纳米磁体中磁化涨落和噪声谱的应力工程

Stress Engineering of Magnetization Fluctuation and Noise Spectra in Low-Barrier Nanomagnets Used as Analog and Binary Stochastic Neurons.

作者信息

Rahman Rahnuma, Bandyopadhyay Supriyo

机构信息

Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.

出版信息

Micromachines (Basel). 2024 Sep 22;15(9):1174. doi: 10.3390/mi15091174.

Abstract

A single-domain nanomagnet, shaped like a thin elliptical disk with , has a double-well potential profile with two degenerate energy minima separated by a barrier of a few kT ( = Boltzmann constant and = absolute temperature). The two minima correspond to the magnetization pointing along the two mutually anti-parallel directions along the major axis of the ellipse. At room temperature, the magnetization fluctuates randomly between the two minima, mimicking telegraph noise. This makes the nanomagnet act as a "binary" stochastic neuron (BSN) with the neuronal state encoded in the magnetization orientation. If the nanomagnet is magnetostrictive, then the barrier can be depressed further by applying (electrically generated) uniaxial stress along the ellipse's major axis, thereby gradually eroding the double-well shape. When the barrier almost vanishes, the magnetization begins to randomly assume any arbitrary orientation (not just along the major axis), making the nanomagnet act as an "analog" stochastic neuron (ASN). The magnetization fluctuation then begins to increasingly resemble white noise. The full width at half maximum (FWHM) of the noise auto-correlation function decreases with increasing stress, as the fluctuation gradually transforms from telegraph noise to white noise. Consistent with this trend, the noise spectral density exhibits a 1/f spectrum (at high frequencies) with β decreasing from 2.00 to 1.88 with increasing stress. Stress can thus not only reconfigure a BSN to an ASN, which has its own applications, but it can also perform "noise engineering", i.e., tune the auto-correlation function and power spectral density, having applications in signal processing.

摘要

一种单畴纳米磁体,形状如同一个薄椭圆盘,其具有双阱势分布,两个简并的能量极小值被一个几kT的势垒隔开(k为玻尔兹曼常数,T为绝对温度)。这两个极小值对应着沿椭圆长轴的两个相互反平行方向的磁化方向。在室温下,磁化强度在这两个极小值之间随机波动,类似电报噪声。这使得纳米磁体充当一个“二进制”随机神经元(BSN),神经元状态编码在磁化方向上。如果纳米磁体是磁致伸缩的,那么通过沿椭圆长轴施加(电产生的)单轴应力可以进一步降低势垒,从而逐渐侵蚀双阱形状。当势垒几乎消失时,磁化强度开始随机地取任意方向(不仅仅是沿长轴方向),使纳米磁体充当一个“模拟”随机神经元(ASN)。然后磁化强度波动开始越来越像白噪声。随着波动从电报噪声逐渐转变为白噪声,噪声自相关函数的半高宽(FWHM)随着应力增加而减小。与这一趋势一致,噪声谱密度在高频时呈现1/f谱,随着应力增加,β从2.00降至1.88。因此,应力不仅可以将一个BSN重新配置为一个ASN,这有其自身的应用,而且还可以进行“噪声工程”,即调整自相关函数和功率谱密度,在信号处理中有应用。

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