神经形态工程学
记忆电阻器
材料科学
钙钛矿(结构)
纳米线
光电子学
横杆开关
纳米技术
欧姆接触
可扩展性
感知器
电极
纳米电子学
计算机科学
图层(电子)
电子工程
整改
电压
热传导
动态范围
电导
振荡(细胞信号)
压阻效应
作者
Swapnadeep Poddar,Biswadeep Khan,Shivam Kumar,Jinghao Li,Yucheng Ding,Zhesi Chen,Kaichen Wang,Chenxi Jin,Jinchen Wei,Zhirong Peng,On-mongkol Jaesiri,Mansun Chan,Julie L. Semmelhack,Zhiyong Fan
标识
DOI:10.1038/s41467-026-71372-5
摘要
Passive memristor crossbars present a high-density and low-power platform for neuromorphic computing. Although perovskite memristors present advantages such as re-configurability between electrochemical metallization and valence change mechanisms compared to traditional oxides, they face issues with uniformity, variability, and filamentary instability, which impede their large-scale integration. This work demonstrates that these limitations are overcome by a three-dimensional perovskite nanowire array architecture, where a precisely engineered ITO barrier is instrumental in maintaining controlled, analog conductance modulation. The resulting memristors exhibit 139 non-overlapping Ohmic conduction states within an optimal analog range (10–100 µS), long retention ( > 10⁶ s), endurance exceeding 4 × 10⁵ cycles, an asymmetric non-linearity factor of 0.3, and minimal variability (device-to-device <5%, cycle-to-cycle <1.5%). The memristors are further integrated into a 64 × 64 crossbar array and utilized in developing a fully integrated multi-layer perceptron for zebrafish head and jaw movement analysis, thereby establishing a scalable pathway for robust perovskite-based neuromorphic hardware. Poddar et al. report perovskite nanowire array-based memristors. A thin ITO layer is introduced as a semi-permeable barrier between electrode and perovskite for controllable analog switching. A physical multi-layer perceptron based on a 64×64 crossbar array enables the analysation of zebrafish strike patterns.
科研通智能强力驱动
Strongly Powered by AbleSci AI