神经形态工程学
材料科学
纳米技术
门控
记忆电阻器
电压
数码产品
光电子学
电化学
逻辑门
计算机科学
人工神经网络
柔性电子器件
电子工程
卷积神经网络
理论(学习稳定性)
纳米电子学
电导率
转导(生物物理学)
生物神经网络
作者
Siyu Sun,Yueyan Zhang,Zhikang Han,Chengjing Liu,Bai Sun,Wei Zhang,Gang He
标识
DOI:10.1002/anie.202523345
摘要
Neuromorphic computing is a bioinspired paradigm that emulates the structure and functionality of biological neural networks, demanding cutting-edge materials and device architectures. In this work, we present a bioinspired electrochemical neuromorphic device (BEND) utilizing a thienoviologen-based electrolyte. The incorporation of thiophene groups into the viologen structure (ThV2+) leads to a reduced energy gap, improved radical stability, and enhanced electrochemical activity. The device exhibits excellent ambient stability and continuously tunable conductivity in response to voltage pulse stimulation. When integrated into a convolutional neural network (CNN) for image recognition, BEND achieves an accuracy of nearly 80% on the Fashion-MNIST dataset. Moreover, the device successfully mimics essential synaptic functions such as spike-timing-dependent plasticity (STDP), Pavlovian learning, and supports dual-terminal logic gate operations. These results significantly expand the functional versatility of viologen-based materials in neuromorphic electronics and offer new insights into the design of next-generation electrochemical artificial synapses.
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