记忆电阻器
控制重构
人工神经网络
纳米技术
逻辑门
神经形态工程学
计算机科学
计算机体系结构
人工智能
电子工程
工程类
嵌入式系统
算法
材料科学
作者
Yixin Ling,Lejian Yu,Ziwen Guo,Fazhou Bian,Yanqiong Wang,Xin Wang,Yaqi Hou,Xu Hou
摘要
The biological neural network is a highly efficient in-memory computing system that integrates memory and logical computing functions within synapses. Moreover, reconfiguration by environmental chemical signals endows biological neural networks with dynamic multifunctions and enhanced efficiency. Nanofluidic memristors have emerged as promising candidates for mimicking synaptic functions, owing to their similarity to synapses in the underlying mechanisms of ion signaling in ion channels. However, realizing chemical signal-modulated logic functions in nanofluidic memristors, which is the basis for brain-like computing applications, remains unachieved. Here, we report a single-pore nanofluidic logic memristor with reconfigurable logic functions. Based on the different degrees of protonation and deprotonation of functional groups on the inner surface of the single pore, the modulation of the memristors and the reconfiguration of logic functions are realized. More noteworthy, this single-pore nanofluidic memristor can not only avoid the average effects in multipore but also act as a fundamental component in constructing complex neural networks through series and parallel circuits, which lays the groundwork for future artificial nanofluidic neural networks. The implementation of dynamic synaptic functions, modulation of logic gates by chemical signals, and diverse combinations in single-pore nanofluidic memristors opens up new possibilities for their applications in brain-inspired computing.
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