Das Dimitra, Assi Dani S, Kazim Samrana, Roy Vellaisamy A L, Ahmad Shahzada
BCMaterials, Basque Center for Materials, Applications, and Nanostructures, UPV/EHU Science Park, 48940, Leioa, Spain.
School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong SAR, 999077, P. R. China.
Mater Horiz. 2025 Jul 23. doi: 10.1039/d5mh00534e.
The von Neumann architecture serves as the foundation for computers by storing data and instructions in the same memory space; however, it limits the data transfer between the CPU and memory. The human brain is an avant-garde organic machine that connects electrical systems with its network of neurons and synapses. We have 80-100 billion neurons, each connected to >1000 other neurons called synapses, thus totalling 100 trillion connections that excel our decision-making and learning processes. Neuromorphic engineering aims to create brain-like devices that operate effectively with low power consumption to supersede von Neumann for faster computation. Despite their efficacy, neuromorphic chips built with CMOS circuits are complex in replicating biological processes. Neuromorphic computing led to the development of memristors to improve performance, flexibility, and scalability. Wonder materials like halide perovskites with both ionic and semiconductive properties mimic synaptic behaviour. Halide perovskites have exceptional ion transport properties, enabling rapid resistive switching for neuromorphic advancement, and furthermore, respond to various stimuli like light and temperature, offering the potential for emulating complex synaptic behaviours. Halide perovskites can modulate functionalities through structural variations, and dimension reduction endorses versatility in neuromorphic computing and future semiconducting technology. Furthermore, we uncover the mechanisms through which halide perovskites emulate synaptic functions in neuromorphic systems.
冯·诺依曼架构通过将数据和指令存储在同一内存空间为计算机奠定了基础;然而,它限制了CPU与内存之间的数据传输。人类大脑是一台前沿的有机机器,通过其神经元和突触网络连接电气系统。我们有800 - 1000亿个神经元,每个神经元都与1000多个称为突触的其他神经元相连,因此总共有100万亿个连接,这使我们的决策和学习过程更加出色。神经形态工程旨在制造出类似大脑的设备,这些设备能以低功耗高效运行,从而取代冯·诺依曼架构以实现更快的计算。尽管基于CMOS电路构建的神经形态芯片具有高效性,但在复制生物过程方面却很复杂。神经形态计算促使忆阻器的发展,以提高性能、灵活性和可扩展性。像卤化物钙钛矿这样兼具离子和半导体特性的神奇材料能够模拟突触行为。卤化物钙钛矿具有卓越的离子传输特性,能够实现快速电阻切换以推动神经形态发展,此外,还能对光和温度等各种刺激做出响应,为模拟复杂的突触行为提供了潜力。卤化物钙钛矿可以通过结构变化来调节功能,尺寸减小则增强了神经形态计算和未来半导体技术的通用性。此外,我们还揭示了卤化物钙钛矿在神经形态系统中模拟突触功能的机制。