Halide Perovskite Based Memory Devices and Neuromorphic Computing

Abstract

The swift rise of digital communications, particularly in the realm of big data and the Internet of Things (IoT), has accelerated the development of next-generation data storage technologies. Resistive switching (RS) memory devices and artificial synapses remain appealing alternatives, offering low power consumption, ability to accommodate various capacities, and rapid speed. Neuromorphic computing aims to simulate the neuronal structure and functioning of the human brain, enabling advancements in human perception, interpretation, and autonomous adaptation. Halide perovskites are a group of materials that possess several benefits, such as a significant distance over which charge carriers can move, strong ability to absorb light, ability to carry both positive and negative charges, ability to conduct ions, and the ability to be processed in solution. Photovoltaic, light-emitting diode, laser, and photodetector are merely some of the numerous areas in which they have proven useful. This article provides an extensive review of the most contemporary advancements in halide perovskite-based artificial synapses and RS memory devices. To begin with, this paper introduces the overall structure and distinctive features of RS memory devices. Next, we delve into the exceptional memory performance supported by comprehensive operational processes. This review also aims at laying the groundwork for the rational development of halide perovskite memory devices and artificial synapses, which will lead to notable performance improvements. Lastly, the present obstacles and the possibilities for future progress are discussed.