Multiferroic neuromorphic computation devices

APL Materials Pub Date : 2024-06-01 DOI:10.1063/5.0216849
Guangming Lu, E. Salje
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Abstract

Neuromorphic computation is based on memristors, which function equivalently to neurons in brain structures. These memristors can be made more efficient and tailored to neuromorphic devices by using ferroelastic domain boundaries as fast diffusion paths for ionic conduction, such as of oxygen, sodium, or lithium. In this paper, we show that the local memristor generates a second, unexpected feature, namely, weak magnetic fields that emerge from moving ferroelastic needle domains and vortices. The vortices appear near ferroelastic “junctions” that are common when the external stimulus is a combination of electric fields and structural phase transitions. Many ferroelastic materials show such phase transitions near room temperatures so that device applications display a “multiferroic” scenario where the memristor is driven electrically and read magnetically. Our computer simulation study of an elastic spring model suggests magnetic fields in the order of 10−7 T, which opens the way for a fundamentally new way of running neuromorphic devices. The magnetism in such devices emerges entirely from intrinsic displacement currents and not from any intrinsic magnetism of the material.
多铁芯神经形态计算设备
神经形态计算基于忆阻器,其功能相当于大脑结构中的神经元。通过利用铁弹性域边界作为离子传导(如氧、钠或锂)的快速扩散路径,可以提高这些忆阻器的效率,并为神经形态设备量身定制。在本文中,我们展示了局部忆阻器产生的第二个意想不到的特征,即从移动的铁弹性针状畴和漩涡中产生的弱磁场。涡旋出现在铁弹性 "结点 "附近,当外部刺激是电场和结构相变的组合时,这种 "结点 "很常见。许多铁弹性材料在室温附近都会出现这种相变,因此设备应用中会出现 "多铁磁 "的情况,即记忆晶闸管由电驱动并由磁读取。我们对弹性弹簧模型的计算机模拟研究表明,磁场的大小约为 10-7 T,这为神经形态设备的运行开辟了一条全新的道路。这种设备中的磁性完全来自于固有的位移电流,而不是材料的固有磁性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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