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High-Speed Ionic Synaptic Memory Based on 2D Titanium Carbide MXene

delete2021-11-21
delete46
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OA
AI
A
Armantas Melianas *
M
Min‐A Kang
A
Armin VahidMohammadi *
T
Tyler J. Quill
W
Weiqian Tian
Y
Yury Gogotsi
A
Alberto Salleo *
M
Mahiar Max Hamedi *
DOI:10.1002/adfm.202109970delete
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摘要

摘要

En 中文
Synaptic devices with linear high-speed switching can accelerate learning in artificial neural networks (ANNs) embodied in hardware. Conventional resistive memories however suffer from high write noise and asymmetric conductance tuning, preventing parallel programming of ANN arrays. Electrochemical random-access memories (ECRAMs), where resistive switching occurs by ion insertion into a redox-active channel, aim to address these challenges due to their linear switching and low noise. ECRAMs using 2D materials and metal oxides however suffer from slow ion kinetics, whereas organic ECRAMs enable high-speed operation but face challenges toward on-chip integration due to poor temperature stability of polymers. Here, ECRAMs using 2D titanium carbide (Ti3C2Tx) MXene that combine the high speed of organics and the integration compatibility of inorganic materials in a single high-performance device are demonstrated. These ECRAMs combine the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of ANNs, and importantly, they are stable after heat treatment needed for back-end-of-line integration with Si electronics. The high speed and performance of these ECRAMs introduces MXenes, a large family of 2D carbides and nitrides with more than 30 stoichiometric compositions synthesized to date, as promising candidates for devices operating at the nexus of electrochemistry and electronics.
Keyword:
2D materials
analog resistive memories
electrochemical random-access memories
linear switching
mixed ionic-electronic conductors
molecular self-assembly
MXenes
neuromorphic computing
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期刊

Advanced Functional Materials 封面图
Advanced Functional Materials
IF:
19
论文数:
3.5W
被引数:
32.1W

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D
Drexel University
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被引数: 2.2W
S
Stanford University
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论文数: 8.2W
被引数: 17.0W
R
Royal Institute of Technology
学者数:
1.8W
论文数: 1.8W
被引数: 25
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