神经形态工程学
记忆电阻器
纳米孔
材料科学
纳米技术
流体学
MNIST数据库
微流控
电导
电子线路
电压
逻辑门
生物电子学
集成电路
离子键合
计算机科学
纳米电子学
光电子学
电子工程
非易失性存储器
纳米流体学
神经传递
生物神经网络
电阻随机存取存储器
突触可塑性
堆栈(抽象数据类型)
离子通道
传输(电信)
人工神经网络
突触重量
作者
Honglin Lv,Rui Liu,Xinxiong Yang,Wei Si,Jingjie Sha,Yunfei Chen,Yin Zhang
出处
期刊:Small
[Wiley]
日期:2026-07-29
卷期号:22 (54): e74904-e74904
摘要
ABSTRACT The human brain exhibits extraordinary computational capabilities, enabled by its intricate network of neuronal interconnections and parallel signal transmission pathways. Nanofluidic memristors, which dynamically modulate ionic conductance through controlled ion transport, have shown promise in emulating synaptic functions. Here, we report an integratable nanofluidic memristor based on a hydrogel covered SiN x nanopore, which modulates its ionic conductance via voltage induced ion concentration polarization. By tuning the diameter of SiN x nanopore, the ionic device functionalities can switch from memristor to capacitor. Moreover, this nanofluidic memristor are capable of mimicking diverse synaptic plasticity behaviors, such as paired‐pulse facilitation/depression and synaptic weight potentiation/depression. Notably, it can serve as a neuromorphic synaptic element for information processing, storage, and encoding, achieving an accuracy of 93.8% in the MNIST handwritten digit classification task. Finally, fluidic memristors are integrated to construct fluidic ionic circuits, which are applied for logic functions. This architecture based on hydrogel covered SiN x nanopore devices offers an innovative strategy for the design of integrated iontronic circuits and provides an experimental foundation for neuromorphic computing in liquid environments.
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