Advanced intelligent systems, including memristors and neuromorphic devices, that render high speed and consume little power are getting increasing attention due to the limitations of Moore’s law and the von Neumann bottleneck. Memristors and transistor-based synaptic devices demonstrate strong potential in neuromorphic computing. Active materials used in high-performance memristors need to show a poor defect-migration barrier and high speed of defect migration. Imperfections in halide perovskites lead to charge trapping and ion migration, and hence, make them potential candidates, among other materials, to be used to fabricate synaptic devices. Metal-halide-perovskite-based artificial synaptic devices are successful in emulating synaptic plasticity and the learning function of the human brain. In this article, the structure, mechanism, and properties of memristors are discussed first, followed by a discussion on recent advancements in halide-perovskite-based memristors and artificial synaptic devices with different configurations (two-terminals and three-terminals) stimulated by light or an electric pulse. Finally, the future opportunities and challenges in the field are presented briefly.

• Received 13 November 2021
• Revised 15 May 2022
• Accepted 9 June 2022

DOI:https://doi.org/10.1103/PhysRevApplied.18.017001