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Electronic synaptic memristor systems have the potential to bring revolutionary change to traditional computer structures and to lay a solid foundation for the development of computer architectures simulating artificial brains. Among them, silver (Ag) or copper (Cu) filament-based memristor devices have increasingly attracted attention due to their excellent functional properties in plasticity and as memristors. However, the randomly dynamic process of nucleation during device fabrication results in nonuniform switching parameters. Here, we demonstrate the viability of a high-performance neuromorphic memristor device based on a carbon conductive filament mechanism, with the advantages of high switching stability and low power consumption. The memristor is also able to emulate faithfully different functions of artificial synapses, including paired-pulse facilitation (PPF) and spike-timing-dependent plasticity (STDP). According to detailed electron energy loss spectroscopy (EELS) and transmission electron microscopy (TEM) characterization, it is confirmed that carbon conductive filaments are formed in aluminum nitride (AlN) films comprising the middle layer of the memristor. First principles calculations provide insight into the energetics of defects involved in the diffusion of carbon atoms into the AlN film. This work probes the viability of a new physical conduction mechanism for use in neuromorphic memristor performance, with evidence of improved device performance.

Carbon quantum dots (QDs) have attracted significant interest due to their excellent electronic properties and wide application prospects. However, the application of carbon QDs has been rarely reported in memristors. Here, a memristor model with carbon conductive filaments (CFs) is proposed for the first time based on carbon quantum dots. The CF-based devices exhibited excellent resistive switching performance, in particular a narrow range of SET and RESET voltages and good power efficiency and retention properties. These devices could also emulate important biological synapse performances, such as the transition from short-term plasticity (STP) to long-term potentiation (LTP) behaviors, long-term depression (LTD) behavior, and four types of spike-timing-dependent plasticity (STDP) learning rules. Interestingly, Pavlovian associative learning functions were also reliably demonstrated in the memristor device (MD). The digit recognition ability of the MDs was evaluated though a single-layer perceptron model, in which the recognition accuracy of digits reached 92.63% after 250 training iterations. The transmission electron microscopy (TEM) results evidenced that the carbon CF was found in the MD at the "ON" state. Thus, this new carbon CF-based mechanism for memristors provides a new idea for achieving better neuromorphic MDs and applications.