Dual Redox‐active Covalent Organic Framework‐based Memristors for Highly‐efficient Neuromorphic Computing
Qiongshan Zhang, Qiang Che, Dongchuang Wu, Yunjia Zhao, Yu Chen, Fuzhen Xuan, Bin Zhang
Abstract
Abstract Organic memristors based on covalent organic frameworks (COFs) exhibit significant potential for future neuromorphic computing applications. The preparation of high‐quality COF nanosheets through appropriate structural design and building block selection is critical for the enhancement of memristor performance. In this study, a novel room‐temperature single‐phase method was used to synthesize Ta−Cu 3 COF, which contains two redox‐active units: trinuclear copper and triphenylamine. The resultant COF nanosheets were dispersed through acid‐assisted exfoliation and subsequently spin‐coated to fabricate a high‐quality COF film on an indium tin oxide (ITO) substrate. The synergistic effect of the dual redox‐active centers in the COF film, combined with its distinct crystallinity, significantly reduces the redox energy barrier, enabling the efficient modulation of 128 non‐volatile conductive states in the Al/Ta−Cu 3 COF/ITO memristor. Utilizing a convolutional neural network (CNN) based on these 128 conductance states, image recognition for ten representative campus landmarks was successfully executed, achieving a high recognition accuracy of 95.13 % after 25 training epochs. Compared to devices based on binary conductance states, the memristor with 128 conductance states exhibits a 45.56 % improvement in recognition accuracy and significantly enhances the efficiency of neuromorphic computing.