神经形态工程学
记忆电阻器
材料科学
纳米尺度
电阻随机存取存储器
光电子学
纳米技术
人工神经网络
导电体
电子能量损失谱
电导
晶体管
透射电子显微镜
传输(电信)
导电原子力显微镜
电子工程
能量(信号处理)
工作(物理)
计算机科学
显微镜
离子
频道(广播)
光谱学
硅烯
德拉姆
电子
人工智能
垂直位移
作者
Y Zhang,Yanghe Wang,Yangchun Tan,Jiaqi Yan,Wenjie Ming,Ke Qu,Zhenzhong Yang,Yuxin Liu,Fengyuan Zhang,Yihang Lei,Miangqiang Huang,Zhi‐Hui Lyu,Lei Liao,Gaokuo Zhong,Changjian Li
标识
DOI:10.1002/adma.202517859
摘要
ABSTRACT Integrating sensing functions into memristors is promising to realize in‐sensor computing with unpreceded energy efficiency and minimized latency. Strain‐sensitive memristors gradually draw attention in neuromorphic tactile sensing applications, but still face the sensitivity‐response time tradeoff dilemma. Here, we demonstrate that antiphase boundaries (APBs) in thin‐film structures function as mechanically gated vertical ion channels in a strain‐sensitive neuromorphic memristor, which can achieve high sensitivity (strain gauge factor of 1.7 × 10 4 ) and rapid response (≤3 ms) while exhibiting synaptic plasticity. Through atomic‐scale scanning transmission electron microscopy (STEM), electron energy loss spectroscopy (EELS), and nanoscale conducting atomic force microscopy (C‐AFM), we confirm APBs as preferential oxygen vacancy migration paths, whose conductivity is dynamically modulated by electrical and mechanical stimuli synchronous and directly. Excellent electrically (25500% on/off ratio at −7 V) and mechanically tunable conductive (enhanced 15167% with ∼3.3 µN force) behavior along APBs pillars is clearly observed. While synapse‐like information processing functions are also further demonstrated with APBs pillars, and a high image classification accuracy (97.7%) within 100 learning epochs is achieved in a two‐level artificial neural network via simulation. This work establishes a potential pathway for integrated sensing and computing systems for next‐generation intelligent robotics and adaptive prosthetics.
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