Security enhancement of artificial neural network using physically transient form of heterogeneous memristors with tunable resistive switching behaviors-Reference-Cited by-同舟云学术

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Security enhancement of artificial neural network using physically transient form of heterogeneous memristors with tunable resistive switching behaviors

Published:2024-07-16 Issue:9 Volume:67 Page:2856-2865
ISSN:2095-8226
Container-title:Science China Materials
language:en
Short-container-title:Sci. China Mater.

Author:

Sun Jing,Wang Zhan,Wang Xinyuan,Zhou Ying,Wang Yanting,He Yunlong,Lei Yimin,Wang Hong,Ma Xiaohua

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40843-024-2951-2.pdf

Reference60 articles.

1. Najmaei S, Glasmann AL, Schroeder MA, et al. Advancements in materials, devices, and integration schemes for a new generation of neuromorphic computers. Mater Today, 2022, 59: 80–106

2. Burr GW, Shelby RM, Sebastian A, et al. Neuromorphic computing using non-volatile memory. Adv Phys-X, 2017, 2: 89–124

3. Merolla PA, Arthur JV, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668–673

4. Milo V, Malavena G, Monzio Compagnoni C, et al. Memristive and CMOS devices for neuromorphic computing. Materials, 2020, 13: 166

5. Rahimi Azghadi M, Chen YC, Eshraghian JK, et al. Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing. Adv Intelligent Syst, 2020, 2: 1900189

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