An energy-efficient memristive synapse is highly desired for the development of brain-like neurosynaptic chips. In this work, a ZnO-based memristive synapse with ultralow-power consumption was achieved by simple N-doping. The introduction of N atoms, as the acceptor, reduces the carrier concentration and greatly increases the resistance of the ZnO film. The low energy consumption, which is as low as 60 fJ per synaptic event, can be achieved in our device. Essential synaptic learning functions have been demonstrated, including excitatory postsynaptic current, paired-pulse facilitation, and experience-dependent learning behaviors. Furthermore, the device can still exhibit the synaptic performance in the bent state or even after 100 bending cycles. Our memristive synapse is not only promising for energy-efficient neuromorphic computing systems but also suitable for the development of wearable neuromorphic electronics.

A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.

The development of electronic devices that possess the functionality of biological synapses is a crucial step towards neuromorphic computing. In this work, we present a WO x -based memristive device that can emulate voltage-dependent synaptic plasticity. By adjusting the amplitude of the applied voltage, we were able to reproduce short-term plasticity (STP) and the transition from STP to long-term potentiation. The stimulation with high intensity induced long-term enhancement of conductance without any decay process, thus representing a permanent memory behavior. Moreover, the image Boolean operations (including intersection, subtraction, and union) were also demonstrated in the memristive synapse array based on the above voltage-dependent plasticity. The experimental achievements of this study provide a new insight into the successful mimicry of essential characteristics of synaptic behaviors.