神经形态工程学
铁磁性
材料科学
自旋电子学
数码产品
可扩展性
纳米技术
太比特
纳米电子学
光电子学
计算机科学
电阻式触摸屏
柔性电子器件
记忆电阻器
电子工程
整改
超短脉冲
电子线路
电气工程
集成电路
钥匙(锁)
隧道磁电阻
非易失性存储器
纳米尺度
计算机体系结构
各向异性
生物电子学
图层(电子)
扭矩
制作
作者
Li Liu,Peixin Qin,Xiang Wang,Xiaobo She,Shaoxuan Zhang,Xiaoning Wang,Hongyu Chen,Guojian Zhao,Zhiyuan Duan,Ziang Meng,Qinghua Zhang,Qiong Wu,Yu Liu,Zhiqi Liu
出处
期刊:Nano Letters
[American Chemical Society]
日期:2025-09-15
卷期号:25 (38): 14213-14221
被引量:2
标识
DOI:10.1021/acs.nanolett.5c04033
摘要
Flexible electronics and neuromorphic computing face key challenges in material integration and function retention. In particular, freestanding membranes suffer from slow sacrificial layer removal and interfacial strain, while neuromorphic hardware often relies on area-intensive dual-device schemes for bipolar synaptic weights. Here, we present a universal strategy based on water-soluble Sr4Al2O7 sacrificial layers, enabling the rapid release of freestanding ferrimagnetic metal membranes, which exhibit deterministic spin-orbit torque switching characteristics with well-preserved perpendicular magnetic anisotropy and are potential for next-generation ultrafast information technology. Extending this approach, we realize single-device ferrimagnetic synapses exhibiting intrinsic bipolar resistive switching. When implemented in a ResNet-18 architecture, these devices achieve 92% accuracy on CIFAR-10─comparable to floating-point software models─while halving device counts relative to differential-pair implementations. These results establish a scalable platform linking flexible spintronics with compact, high-performance neuromorphic systems, offering foundational advances for next-generation electronics and brain-inspired hardware.
科研通智能强力驱动
Strongly Powered by AbleSci AI