All‐van der Waals‐Integrated Memristors Based on Atomically Thin MoS 2‐ x O x Film for LIF Neurons and Nociceptive Receptors

ABSTRACT Atomically thin two‐dimensional (2D) materials have emerged as ideal candidates for realizing high‐density, fast switching, and energy‐efficient memristors. However, ultrathin memristive interlayers, particularly at the atom scale, typically suffer from poor endurance and significant variations. Herein, we demonstrate wafer‐scale high‐quality MoS 2‐ x O x films achieved by incorporating oxygen into monolayer MoS 2 via a controlled thermal treatment. The optimized oxygen content and low defect density endow the MoS 2‐ x O x memristors with better switching characteristics compared to MoS 2 memristors. Furthermore, systematic interface engineering improves device reproducibility through an all‐van der Waals (vdW) integrated Ag/MoS 2‐ x O x /Al 2 O 3 /Al architecture, where an ultrathin Al 2 O 3 film serves as a buffer layer. Notably, the Al 2 O 3 layer formed by native oxidation was a compact self‐aligned structure that facilitates high‐density integration without requiring complex fabrication steps. The optimized device demonstrates balanced memristor performances with a memory window exceeding 10 3 , and a low operating voltage of 0.47 ± 0.07 V during continuous 100 curve sweeping and exceeding 1800 cycles under pulses test. In addition, the device based on monolayer MoS 2‐ x O x successfully emulated LIF neurons and nociceptors, which lays the foundation for the application of memristors in artificial neural networks and humanoid robots.

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Publication Details

Journal
Rare Metals
Published
2026-10-26
DOI
https://doi.org/10.1002/rar2.70666
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

All‐van der Waals‐Integrated Memristors Based on Atomically Thin MoS 2‐ x O x Film for LIF Neurons and Nociceptive Receptors

Biao Qin, Jia Li, Shiyu Chen, Yujie Bai et al.
Rare Metals
Advanced Memory and Neural Computing
article

All‐van der Waals‐Integrated Memristors Based on Atomically Thin MoS 2‐ x O x Film for LIF Neurons and Nociceptive Receptors

Biao Qin, Jia Li, Shiyu Chen, Yujie Bai, Xidong Duan, Yuhao Kong, Mouyuan Liu, Siling Liu, Ziao Tian, Wen Huang, Xiangdong Yang, Jianhong Yang, Weiyou Yang
article en

Abstract

ABSTRACT Atomically thin two‐dimensional (2D) materials have emerged as ideal candidates for realizing high‐density, fast switching, and energy‐efficient memristors. However, ultrathin memristive interlayers, particularly at the atom scale, typically suffer from poor endurance and significant variations. Herein, we demonstrate wafer‐scale high‐quality MoS 2‐ x O x films achieved by incorporating oxygen into monolayer MoS 2 via a controlled thermal treatment. The optimized oxygen content and low defect density endow the MoS 2‐ x O x memristors with better switching characteristics compared to MoS 2 memristors. Furthermore, systematic interface engineering improves device reproducibility through an all‐van der Waals (vdW) integrated Ag/MoS 2‐ x O x /Al 2 O 3 /Al architecture, where an ultrathin Al 2 O 3 film serves as a buffer layer. Notably, the Al 2 O 3 layer formed by native oxidation was a compact self‐aligned structure that facilitates high‐density integration without requiring complex fabrication steps. The optimized device demonstrates balanced memristor performances with a memory window exceeding 10 3 , and a low operating voltage of 0.47 ± 0.07 V during continuous 100 curve sweeping and exceeding 1800 cycles under pulses test. In addition, the device based on monolayer MoS 2‐ x O x successfully emulated LIF neurons and nociceptors, which lays the foundation for the application of memristors in artificial neural networks and humanoid robots.

Rare MetalsVol. 45(11)
Chongqing University (CN), Ningbo University of Technology (CN), Hunan University (CN), Chinese Academy of Sciences (CN), Shanghai Institute of Microsystem and Information Technology (CN), The Synergetic Innovation Center for Advanced Materials (CN), Lanzhou University (CN)
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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