Synaptic and resistive switching behaviors in NiO/Cu2O heterojunction memristor for bioinspired neuromorphic computing

Artificial neural network-based computing prospectively overcomes the von Neumann bottleneck of conventional computers and significantly improves computational efficiency, which shows a wide range of application prospects. Here the NiO/Cu2O memristor is fabricated by magnetron sputtering, which enables functions that emulate biological synapses, such as short/long plasticity, paired-pulse facilitation, and spike timing-dependent plasticity, etc. Furthermore, a artificial neural network based on synaptic weight modulation was presented at the Mixed National Institute of Standards and Technology (MNIST) with recognition accuracy of up to 96.84 % on average, and the device proved able to simulate an array of trainable memristors for image information processing. The results demonstrate the potential of artificial synapses in artificial intelligence systems that incorporate neuromorphological computations and synaptic neural functions.

Automated summary

Artificial neural network-based computing has the potential to overcome the limitations of conventional computers and has a wide range of applications. By using NiO/Cu2O memristors to emulate biological synapses, the recognition accuracy of an artificial neural network based on synaptic weight modulation reached up to 96.84% on average, demonstrating the potential of artificial synapses in artificial intelligence systems.