矩形天线
整流器(神经网络)
能量收集
光电子学
电压
整改
阻抗匹配
材料科学
电气工程
电阻抗
功率(物理)
物理
工程类
计算机科学
量子力学
随机神经网络
机器学习
循环神经网络
人工神经网络
作者
Raghav Sharma,Tung Ngo,Eleonora Raimondo,A. Giordano,Junta Igarashi,Butsurin Jinnai,Shishun Zhao,Jiayu Lei,Yong‐Xin Guo,Giovanni Finocchio,Shunsuke Fukami,Hideo Ohno,Hyunsoo Yang
标识
DOI:10.1038/s41928-024-01212-1
摘要
Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and thermodynamic limits and high-frequency parasitic impedance restrict the performance of state-of-the-art radiofrequency rectifiers. Nanoscale spin rectifiers based on magnetic tunnel junctions have recently demonstrated high sensitivity, but suffer from a low a.c.-to-d.c. conversion efficiency (less than 1%). Here, we report a sensitive spin rectifier rectenna that can harvest ambient radiofrequency signals between -62 and -20 dBm. We also develop an on-chip co-planar waveguide-based spin rectifier array with a large zero-bias sensitivity (around 34,500 mV/mW) and high efficiency (7.81%). Self-parametric excitation driven by voltage-controlled magnetic anisotropy is a key mechanism that contributes to the performance of the spin-rectifier array. We show that these spin rectifiers can wirelessly power a sensor at a radiofrequency power of -27 dBm.
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