A Digital–Analog Integrated Memristor Based on a ZnO NPs/CuO NWs Heterostructure for Neuromorphic Computing

As an emerging electronic device, a memristor facilitates the realization of neuromorphic computing systems. However, high-performance neuromorphic computing systems require not only robust analog memristors but also digital memristors with complementary functions. Here, a digital–analog integrated memristor based on Al/ZnO NPs/CuO NWs/Cu is proposed and demonstrated. First, the electrical properties of the CuO NWs based device are investigated, which exhibit a write-once-read-many-times (WORM) performance. Then, typical bipolar resistive switching behaviors are realized by spin-coating the ZnO NPs to form an Al/ZnO NPs/CuO NWs/Cu based memristor. Through tuning of the applied voltage, an abrupt-to-gradient transition of conductance is achieved in situ. Several analog behaviors, such as long-term potentiation/depression (LTP/LTD), and paired-pulse facilitation/depression (PPF/PPD) are effectively emulated by applying a series of specified electrical measurements to the memristor, which proves the achievement of a digital–analog integrated memristor. Furthermore, on the basis of the LTD and LTP behaviors of the memristor, neural network simulations for handwritten recognition are implemented. The results reveal high recognition accuracies of up to 93%, which are close to the performances for ideal memristors.