Cu intercalation and field-driven filament formation in vertically aligned MoS$_2$ memristors

Vertically-aligned MoS$_2$ films paired with a Cu top electrode have been used as electrochemical metallization cell memristor devices. The memristive switching has been attributed to the formation of conductive filaments, yet their exact structure has remained elusive. Here we carry out a multi-scale computational study of Cu intercalation, clustering, and field-driven dynamics to eventually model the filament formation under operating conditions. We use density-functional theory results to fine-tune a machine learning force field, and the most important physical phenomena are further distilled into multi-particle kinetic Monte Carlo simulations. By exhaustive enumeration of Cu intercalated structures, we find the most stable structure at MoS$_2$Cu$_{0.11}$ composition, with Cu atoms forming a dice lattice between the MoS$_2$ layers. These structures are shown to exhibit metallic character and can thus form conductive filaments under memristor operation. The simulated switching speed and threshold voltage agree well with the available experimental data. Our results give fundamental insights on the device operation mechanism and guidelines for maximizing switching speed and endurance.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-27
DOI
https://doi.org/10.48550/arxiv.2610.37152
Primary Topic
Advanced Memory and Neural Computing
Type
preprint
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preprint

Cu intercalation and field-driven filament formation in vertically aligned MoS$_2$ memristors

Hannu‐Pekka Komsa, Touko Lehenkari
arXiv (Cornell University)
Advanced Memory and Neural Computing
preprint

Cu intercalation and field-driven filament formation in vertically aligned MoS$_2$ memristors

Hannu‐Pekka Komsa, Touko Lehenkari
preprint en

Abstract

Vertically-aligned MoS$_2$ films paired with a Cu top electrode have been used as electrochemical metallization cell memristor devices. The memristive switching has been attributed to the formation of conductive filaments, yet their exact structure has remained elusive. Here we carry out a multi-scale computational study of Cu intercalation, clustering, and field-driven dynamics to eventually model the filament formation under operating conditions. We use density-functional theory results to fine-tune a machine learning force field, and the most important physical phenomena are further distilled into multi-particle kinetic Monte Carlo simulations. By exhaustive enumeration of Cu intercalated structures, we find the most stable structure at MoS$_2$Cu$_{0.11}$ composition, with Cu atoms forming a dice lattice between the MoS$_2$ layers. These structures are shown to exhibit metallic character and can thus form conductive filaments under memristor operation. The simulated switching speed and threshold voltage agree well with the available experimental data. Our results give fundamental insights on the device operation mechanism and guidelines for maximizing switching speed and endurance.

arXiv (Cornell University)
Advanced Memory and Neural Computing
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