Intermediate Resistive State in Wafer‐Scale Vertical MoS <sub>2</sub> Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

ABSTRACT Memristors based on 2D materials have garnered significant attention. Their fast resistive switching (RS) behavior and atomic‐level thickness enable low power consumption, making them promising candidates for neuromorphic computing. Among these, memristors based on molybdenum disulfide (MoS 2 ) have been extensively studied. Their RS has been attributed to the formation and rupture of conductive filaments (CFs). However, the underlying mechanism of filament formation remains underexplored, and the inherently stochastic nature of RS leads to high variability and limited reproducibility. In this study, we demonstrate wafer‐scale memristors based on metal–organic chemical vapor‐deposited MoS 2 . Our devices exhibit both volatile and nonvolatile RS behaviors, tunable by modulating the current compliance. Notably, we observe stable RS characteristics in an intermediate resistive state (IRS), set and reset voltages within ±1 V, endurance of over 2500 cycles, and state retention over 10 6 s. Among 100 tested devices, 98 exhibited IRS behavior, confirming a high yield (98%) and demonstrating its robust and reproducible RS nature. Combined experimental and simulation results further indicate that the IRS arises from the lateral growth of CFs in the van der Waals gaps. Furthermore, the devices successfully emulate synaptic plasticity with current responses on a microsecond timescale, highlighting their potential for large‐scale integration in neuromorphic computing architectures.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-12-30
DOI
https://doi.org/10.1002/adfm.202526682
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46

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article

Intermediate Resistive State in Wafer‐Scale Vertical MoS 2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

Lukas Völkel, Milan Buttberg, J. Lee, Joachim Mayer et al.
2 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
1.46
article

Intermediate Resistive State in Wafer‐Scale Vertical MoS 2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

Lukas Völkel, Milan Buttberg, J. Lee, Joachim Mayer, Ilia Valov, Rana Walied Ahmad, Bárbara Canto, M. Heuken, H. Kalisch, T. Wahlbrink, Bart Macco, Menzel Stephan, Holger Lerch, Max C. Lemme, Yuan Fa, Zhenxing Wang, Dennis Braun, Ke Ran, Sofía Cruces, Andrei Vescan
article en
2 citations

Abstract

ABSTRACT Memristors based on 2D materials have garnered significant attention. Their fast resistive switching (RS) behavior and atomic‐level thickness enable low power consumption, making them promising candidates for neuromorphic computing. Among these, memristors based on molybdenum disulfide (MoS 2 ) have been extensively studied. Their RS has been attributed to the formation and rupture of conductive filaments (CFs). However, the underlying mechanism of filament formation remains underexplored, and the inherently stochastic nature of RS leads to high variability and limited reproducibility. In this study, we demonstrate wafer‐scale memristors based on metal–organic chemical vapor‐deposited MoS 2 . Our devices exhibit both volatile and nonvolatile RS behaviors, tunable by modulating the current compliance. Notably, we observe stable RS characteristics in an intermediate resistive state (IRS), set and reset voltages within ±1 V, endurance of over 2500 cycles, and state retention over 10 6 s. Among 100 tested devices, 98 exhibited IRS behavior, confirming a high yield (98%) and demonstrating its robust and reproducible RS nature. Combined experimental and simulation results further indicate that the IRS arises from the lateral growth of CFs in the van der Waals gaps. Furthermore, the devices successfully emulate synaptic plasticity with current responses on a microsecond timescale, highlighting their potential for large‐scale integration in neuromorphic computing architectures.

Advanced Functional Materials
Forschungszentrum Jülich (DE), Bulgarian Academy of Sciences (BG), Aixtron (Germany) (DE), Ernst Ruska Centre (DE), Institute of Electrochemistry and Energy Systems (BG), AMO (Germany) (DE), Jülich Aachen Research Alliance (DE), Eindhoven University of Technology (NL), RWTH Aachen University (DE)
European Commission, Deutsche Forschungsgemeinschaft, HORIZON EUROPE Framework Programme
Affordable and clean energy
Openalex Percentile: Top 14%
Advanced Memory and Neural Computing
1.46
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