Van der Waals materials-based floating gate memory for neuromorphic computing

神经形态工程学 小型化 计算机科学 冯·诺依曼建筑 计算机数据存储 计算机体系结构 计算 纳米技术 计算科学 材料科学 电子工程 人工智能 计算机硬件 人工神经网络 工程类 算法 操作系统
作者
Qianyu Zhang,Zirui Zhang,Ce Li,Renjing Xu,Dongliang Yang,Linfeng Sun
出处
期刊:Chip [Elsevier]
卷期号:2 (4): 100059-100059 被引量:31
标识
DOI:10.1016/j.chip.2023.100059
摘要

With the advent of the "Big Data Era", the rapid growth in different types of data makes it urgent to improve data storage density and computation speed. Flash memory with a floating gate (FG) structure is attracting attention due to its miniaturization, low power consumption, and reliable data storage, which is very effective in solving the problems of large data capacity and high integration density. Meanwhile, the FG memory with charge storage principle can simulate synaptic plasticity perfectly, which helps to break the traditional von Neumann computing architecture and is used as an artificial synapse for neuromorphic computations inspired by the human brain. Among many candidate materials for manufacturing devices, van der Waals (vdW) materials have attracted widespread attention due to their atomic thickness, high mobility, and sustainable miniaturization properties. VdW heterostructure combines rich physics and potential 3D integration due to the arbitrary stacking ability, opening up various possibilities for new functional integrated devices with low power consumption and flexible applications. This paper provides a comprehensive review of memory devices based on vdW materials with FG structure, including the working principles and typical structures of FG structure devices, focusing on various high-performance FG memories and their versatile applications in neuromorphic computing. Finally, the challenges of neuromorphic devices based on FG structures are discussed. This review will shed light on the design and fabrication of vdW material-based memory devices with FG engineering, helping to advance the development of practical and promising neuromorphic computing.
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