用于高性能神经形态器件的楔形Pt/Co/Ta无场自旋-轨道转矩开关。

IF 8.2 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Xi Guo, , , Junwei Zeng, , , Jijun Yun*, , , Yalu Zuo, , , Li Xi, , , Xiaoxi Liu*, , and , Baoshan Cui*, 
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引用次数: 0

摘要

在垂直磁化的重金属/铁磁异质结构中,自旋轨道转矩诱导的磁矩多电平开关为非易失性多电平信息存储提供了广阔的应用前景。与传统的二进制存储体系结构相比,该方法提高了数据密度,降低了功耗。然而,在垂直磁化异质结构中需要额外的面内磁场来破坏磁对称,这阻碍了自旋轨道转矩驱动磁化开关器件的集成。本文研究了斜溅射Pt/Co/Ta异质结构中的无场自旋-轨道转矩转换,证明了斜沉积角决定了磁矩的倾斜,从而实现了无场切换。通过调节电流脉冲的宽度和幅度,我们实现了多种阻抗状态,模拟了突触行为,如长期增强/长期抑制和突触后兴奋/抑制电位。此外,磁化开关曲线的线性优化显示了它们与整流线性单元激活函数的兼容性,进一步突出了该方法在人工神经网络应用中的通用性。最后,利用无场自旋-轨道扭矩装置的全连接卷积神经网络在CIFAR-10数据集上取得了令人印象深刻的93.38%的分类准确率,证明了该技术在高级机器学习任务中的实际可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Field-Free Spin–Orbit Torque Switching in Wedge-Shaped Pt/Co/Ta for High-Performance Neuromorphic Devices

Field-Free Spin–Orbit Torque Switching in Wedge-Shaped Pt/Co/Ta for High-Performance Neuromorphic Devices

In perpendicularly magnetized heavy-metal/ferromagnetic heterostructures, the spin–orbit torque-induced multilevel switching of magnetic moment offers promising potential for nonvolatile multilevel information storage. Compared with traditional binary memory architectures, this approach provides improved data density and lower power consumption. However, the necessity of an additional in-plane magnetic field to break magnetic symmetry in perpendicularly magnetized heterostructures impedes the integration of spin–orbit torque-driven magnetization switching devices. This work investigates field-free spin–orbit torque switching in obliquely sputtered Pt/Co/Ta heterostructures, demonstrating that the oblique deposition angle determines the tilt of magnetic moments, thereby enabling field-free switching. By adjusting the width and amplitude of current pulses, we implemented multiple resistance states, mimicking synaptic behaviors such as long-term potentiation/long-term depression and excitatory/inhibitory postsynaptic potentials. Besides, linear optimization of magnetization switching curves revealed their compatibility with the rectified linear unit activation function, further highlighting the versatility of this approach for application in artificial neural networks. Finally, a fully connected convolutional neural network utilizing field-free spin–orbit torque devices achieved an impressive classification accuracy of 93.38% on the CIFAR-10 data set, demonstrating the practical feasibility of this technology in advanced machine learning tasks.

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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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