神经形态工程学
灵敏度(控制系统)
整改
磁场
光电子学
扭矩
二极管
自旋(空气动力学)
电流密度
材料科学
电压
电气工程
微波食品加热
凝聚态物理
计算机科学
物理
电子工程
人工神经网络
工程类
电信
人工智能
量子力学
热力学
作者
Like Zhang,Huayao Tu,Yanxiang Luo,Kexin Zeng,Xu Tao,Dong Zhao,Bin Fang,Zhongming Zeng
摘要
Spin-torque diodes (STDs) offer the possibility of using spin torque to generate rectification voltage with promising applications in microwave detecting, energy harvesting, and neuromorphic computing. Here, we demonstrate a highly sensitive STD with ultralow current density based on a magnetic tunnel junction with perpendicular magnetic anisotropy. At zero magnetic field, a high sensitivity exceeding 3785 V/W is obtained with a low current of −20 μA, corresponding to a current density of ∼105 A/cm2, which is one order lower than the previously reported. When a weak external magnetic field is applied, the sensitivity can be further increased by five times to 20 000 V/W. Furthermore, we construct an artificial neural network with STD neurons to perform recognition of handwritten digits in the Mixed National Institute of Standards and Technology database, where a produced accuracy of up to 94.92% is obtained. Our work provides a route to develop low-power consumption high-sensitivity STDs for Internet of Things applications and neuromorphic computing.
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