MoS2 quantum capacitance for memcapacitor

While memcapacitors exhibit scalability, a sneak-path-free nature, and lower power consumption compared to memristors, the sensitivity and responsiveness of conventional memcapacitors are lower than expected due to their physical mechanisms. Quantum capacitance, which is controlled by the Fermi level and the density of states, holds the potential to enhance memcapacitor performance and address these issues. In this study, a unique memcapacitor based on the MoS2 quantum capacitance (MoS2 quantum memcapacitor, MQM) is proposed, and the “learning-forgetting” behavior of simulated synapses is discussed. Initially, the quantum capacitance memory effect of MoS2 was demonstrated. Subsequently, under gate voltage (Vg) control, the MQM exhibited a sensitive response and excellent repeatability. Finally, the MQM demonstrated a range of artificial synaptic behaviors consistent with biological synapses, including transformations from short-term potentiation to long-term potentiation and from short-term depression to long-term depression, as well as high pair-pulse-facilitation behavior. This showcases its excellent versatility and flexibility, thereby promoting the development of an integrated artificial neural network.

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Institutions

Publication Details

Journal
Applied Physics Letters
Published
2024-09-30
DOI
https://doi.org/10.1063/5.0218765
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

MoS2 quantum capacitance for memcapacitor

Yadong Qiao, Yuhang Wang, Chenya Feng, Fengping Wang et al.
5 citations
Applied Physics Letters
Advanced Memory and Neural Computing
1.04
article

MoS2 quantum capacitance for memcapacitor

Yadong Qiao, Yuhang Wang, Chenya Feng, Fengping Wang, Wei Guo, Fadi Wang, Jialu Li, Li Yao
article en
5 citations

Abstract

While memcapacitors exhibit scalability, a sneak-path-free nature, and lower power consumption compared to memristors, the sensitivity and responsiveness of conventional memcapacitors are lower than expected due to their physical mechanisms. Quantum capacitance, which is controlled by the Fermi level and the density of states, holds the potential to enhance memcapacitor performance and address these issues. In this study, a unique memcapacitor based on the MoS2 quantum capacitance (MoS2 quantum memcapacitor, MQM) is proposed, and the “learning-forgetting” behavior of simulated synapses is discussed. Initially, the quantum capacitance memory effect of MoS2 was demonstrated. Subsequently, under gate voltage (Vg) control, the MQM exhibited a sensitive response and excellent repeatability. Finally, the MQM demonstrated a range of artificial synaptic behaviors consistent with biological synapses, including transformations from short-term potentiation to long-term potentiation and from short-term depression to long-term depression, as well as high pair-pulse-facilitation behavior. This showcases its excellent versatility and flexibility, thereby promoting the development of an integrated artificial neural network.

Applied Physics LettersVol. 125(14)
University of Science and Technology Beijing (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
1.04
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MoS2 quantum capacitance for memcapacitor — Yadong Qiao, Yuhang Wang, et al. · Applied Physics Letters (2024) | TGRS Research Map | TGRS