记忆电阻器
钙钛矿(结构)
神经形态工程学
卤化物
计算机科学
材料科学
边缘计算
GSM演进的增强数据速率
纳米技术
边缘设备
工程物理
计算机体系结构
人工神经网络
电子工程
人工智能
工程类
化学
云计算
无机化学
化学工程
操作系统
作者
Tianwei Duan,Jiajia Zha,Ning Lin,Zhongrui Wang,Chaoliang Tan,Yuanyuan Zhou
标识
DOI:10.1016/j.device.2023.100221
摘要
The rapid expansion of artificial intelligence and the internet of things demands an unprecedentedly high level of efficiency in edge computing. Processing-in-memory sensors, based on memristors, play a pivotal role in addressing these challenges, facilitating real-time decision-making, data optimization, and energy efficiency. Despite widespread interest, there’s a dearth of innovation in memristor materials. Metal halide perovskites offer distinct advantages over mainstream memristive materials due to their lower formation energy and strong interaction with light, positioning them as an integrated optoelectronic platform for in-memory computing sensors. In this work, we review metal halide perovskite memristors emphasizing materials, devices, and applications. We discuss their mixed ionic and electronic properties and optoelectronically induced conduction channel formation. Furthermore, we examine device structures, memristive mechanisms, and synapse-like behaviors, together with an exploration of the potential for edge computing applications. Finally, we present our vision for their future development from both hardware and software perspectives.
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