Cation‐Driven Valence Change Mechanism in 2D AgCrS <sub>2</sub> for Ultralow‐Power and Reliable Memristors

Memristive devices are promising building blocks for next-generation memory and neuromorphic circuits in artificial intelligence. Among them, filamentary memristors offer great potential for high-performance and densely integrated systems. However, achieving both low-power operation and long-term cycling stability remains a key challenge. Here, we present a 2D AgCrS<sub>2</sub> volatile memristor that operates via a novel cation-driven valence change mechanism (CVCM). Unlike traditional filament-based conduction, this mechanism enables Ag<sup>+</sup>-driven switching without metal filament growth. The threshold switching process is governed by the reversible intercalation of highly mobile Ag<sup>+</sup> ions into tetrahedral vacancies between CrS<sub>2</sub> layers, forming and rupturing the highly conductive Ag<sub>2</sub>CrS<sub>2</sub> pathway and thus delivering an on/off ratio exceeding 10<sup>5</sup> at 0.1 V. The AgCrS<sub>2</sub> memristor enables a reduced threshold voltage of 0.2 V and an ultralow power consumption down to 200 pW when the compliance current is further reduced to the nA level. Additionally, the absence of elemental Ag metallization in the switching layer prevents structural degradation, enabling stable operation for over 3 × 10<sup>5</sup> switching cycles. These findings establish CVCM as a promising way for developing energy-efficient and reliable memristive technologies.

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Publication Details

Journal
Advanced Science
Published
2026-02-04
DOI
https://doi.org/10.1002/advs.202521409
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
21.93

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article

Cation‐Driven Valence Change Mechanism in 2D AgCrS 2 for Ultralow‐Power and Reliable Memristors

Jing Peng, Changzheng Wu, Yueqi Su, Minghao Wang et al.
1 citations
Advanced Science
Advanced Memory and Neural Computing
21.93
article

Cation‐Driven Valence Change Mechanism in 2D AgCrS 2 for Ultralow‐Power and Reliable Memristors

Jing Peng, Changzheng Wu, Yueqi Su, Minghao Wang, Yi Xie, Y. I.Ping Lin, Yuqiao Guo, Xiaolin Tai, Yuhua Liu
article en
1 citations

Abstract

Memristive devices are promising building blocks for next-generation memory and neuromorphic circuits in artificial intelligence. Among them, filamentary memristors offer great potential for high-performance and densely integrated systems. However, achieving both low-power operation and long-term cycling stability remains a key challenge. Here, we present a 2D AgCrS<sub>2</sub> volatile memristor that operates via a novel cation-driven valence change mechanism (CVCM). Unlike traditional filament-based conduction, this mechanism enables Ag<sup>+</sup>-driven switching without metal filament growth. The threshold switching process is governed by the reversible intercalation of highly mobile Ag<sup>+</sup> ions into tetrahedral vacancies between CrS<sub>2</sub> layers, forming and rupturing the highly conductive Ag<sub>2</sub>CrS<sub>2</sub> pathway and thus delivering an on/off ratio exceeding 10<sup>5</sup> at 0.1 V. The AgCrS<sub>2</sub> memristor enables a reduced threshold voltage of 0.2 V and an ultralow power consumption down to 200 pW when the compliance current is further reduced to the nA level. Additionally, the absence of elemental Ag metallization in the switching layer prevents structural degradation, enabling stable operation for over 3 × 10<sup>5</sup> switching cycles. These findings establish CVCM as a promising way for developing energy-efficient and reliable memristive technologies.

Advanced ScienceVol. 13(18)
Collaborative Innovation Center of Chemistry for Energy Materials (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, University of Science and Technology of China, Natural Science Foundation of Anhui Province, National Key Research and Development Program of China, Youth Innovation Promotion Association
Affordable and clean energy
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
21.93
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