Carbon quantum dots (CQDs) have been used in memristors due to their attractive optical and electronic properties, which are considered candidates for brain-inspired computing devices. In this work, the performance of CQDs-based memristors is improved by utilizing nitrogen-doping. In contrast, nitrogen-doped CQDs (N-CQDs)-based optoelectronic memristors can be driven with smaller programming voltages (–0.6 to 0.7 V) and exhibit lower powers (78 nW/0.29 μW). The physical mechanism can be attributed to the reversible transition between C–N and C=N with lower binding energy induced by the electric field and the generation of photogenerated carriers by ultraviolet light irradiation, which adjusts the conductivity of the initial N-CQDs to implement resistance switching. Importantly, the convolutional image processing based on various cross kernels is efficiently demonstrated by stable multi-level storage properties. An N-CQDs-based optoelectronic reservoir computing implements impressively high accuracy in both no noise and various noise modes when recognizing the Modified National Institute of Standards and Technology (MNIST) dataset. It illustrates that N-CQDs-based memristors provide a novel strategy for developing artificial vision system with integrated in-memory sensor and computing.

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.