Carbon-based memristors for resistive random access memory and neuromorphic applications DOI Creative Commons
Fan Yang,

Zhaorui Liu,

Xumin Ding

et al.

Chip, Journal Year: 2024, Volume and Issue: 3(2), P. 100086 - 100086

Published: Feb. 2, 2024

As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction electronic devices due to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods excellent properties. Especially recent years, memristors based on flourished field building non-volatile memory neuromorphic applications. In this paper, preparation structural characteristics different dimensions are systematically reviewed. Then, working mechanism discussed depth. Finally, potential applications carbon-based logic operations, neural network construction, artificial vision systems, tactile multimodal perception systems introduced. We believe paper will provide guidance for future development high-quality information storage, high-performance applications, high-sensitivity bionic sensing memristors.

Language: Английский

Multifunctional memristors based on N-doped Nb2C MXene nanosheets for neuromorphic computing DOI

Jingxi Gou,

Yuexin Li,

D Zhang

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179892 - 179892

Published: March 1, 2025

Language: Английский

Citations

0

Biomimetic synaptic ion release-capture strategy and related functional materials and device DOI

Ruipeng Shen,

Yingai Li,

Weiran Zhang

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161819 - 161819

Published: March 1, 2025

Language: Английский

Citations

0

Quantum dot-based memristors for information processing and artificial intelligence applications DOI

Daoming Tian,

Chuan Ke, Bai Sun

et al.

Nanoscale, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Research and progress in quantum dot memristors.

Language: Английский

Citations

0

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing DOI Creative Commons
Shao‐Xiang Go, Kian Guan Lim, Tae‐Hoon Lee

et al.

Small Science, Journal Year: 2024, Volume and Issue: 4(3)

Published: Jan. 22, 2024

Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy time, it is costly to shuffle data between entity, for data‐intensive applications associated with artificial intelligence, demand ever increasing. A paradigm shift traditional architectures required, in‐memory computing one of non‐von‐Neumann strategies. By harnessing physical signatures memory, workloads administered same element. For computing, a wide range memristive material (MM) systems have been examined. Moreover, developing schemes that perform sensory network minimize unit sensing element requirement, process large volumes efficiently decrease consumption. In this review, an overview switching character system signature harnessed three archetypal MM rendered, along integrated application survey in‐sensor viz., brain‐inspired or analogue unclonable functions, random number generators. The recent progress theoretical studies reveal structural origin fast‐switching ability further summarized.

Language: Английский

Citations

3

Carbon-based memristors for resistive random access memory and neuromorphic applications DOI Creative Commons
Fan Yang,

Zhaorui Liu,

Xumin Ding

et al.

Chip, Journal Year: 2024, Volume and Issue: 3(2), P. 100086 - 100086

Published: Feb. 2, 2024

As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction electronic devices due to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods excellent properties. Especially recent years, memristors based on flourished field building non-volatile memory neuromorphic applications. In this paper, preparation structural characteristics different dimensions are systematically reviewed. Then, working mechanism discussed depth. Finally, potential applications carbon-based logic operations, neural network construction, artificial vision systems, tactile multimodal perception systems introduced. We believe paper will provide guidance for future development high-quality information storage, high-performance applications, high-sensitivity bionic sensing memristors.

Language: Английский

Citations

3