Dual Redox-active Covalent Organic Framework-based Memristors for Highly-efficient Neuromorphic Computing.

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-Cu3 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-Cu3 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.