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As an emerging information device that adapts to development of the big data era, memristor has attracted much attention due to its advantage in processing massive data. However, the nucleation and growth of conductive filaments often exhibit randomness and instability, which undoubtedly leads to a wide and discrete range of switching parameters, damaging the electrical performance of device. In this work, a strategy of inserting carbon quantum dots (CQDs) into graphene oxide (GO) resistance layer is utilized to improve the stability of the switching parameters and the reliability of the device is improved. Compared with GO-based devices, GO/CQDs/GO-based devices exhibit a more stable resistance switching curve, low power, lower and more concentrated threshold voltage parameters with lower variation coefficient, faster switching speed, and more stable retention and endurance. The cause-inducing performance improvement may be attributed to the local electric field generated by CQDs in resistance switching that effectively guides the formation and rupture of conductive filaments, which optimizes the effective migration distance of Ag+, thereby improving the uniformity of resistance switching. Additionally, a convolutional neural network model is constructed to identify the CIFAR-10 data set, showing the high recognition accuracy of online and offline learning. The cross-kernel structure is used to further implement convolutional image processing through multiplication and accumulation operations. This work provides a solution to improve the performance of memristors, which can contribute to developing digital information processing.
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