Doping-induced performance optimization in monolayer WS<sub>2</sub> memristor: reduced variability and contact resistance

The memristor is a cornerstone for developing novel non-volatile memory devices that enable brain-like efficient processing and storage capabilities. Two-dimensional transition metal dichalcogenide (TMDC)-based memristors are gaining increasing attention due to the advantages they present over their bulk counterparts. In this work, we employed first-principles calculations to demonstrate that dopants play a significant role in reducing the cycle-to-cycle variability and in lowering the contact resistance in monolayer WS<sub>2</sub>-based memristor. The possibility of reduced cycle-to-cycle variability is reflected by the attractive nature of the calculated interaction energy between dopant metal atoms and a sulphur monovacancy in the WS<sub>2</sub> monolayer. The potential for reduced contact resistance is evident from the reduced tunneling barrier heights and increased tunneling probabilities at the electrode/WS<sub>2</sub> interface upon doping. Additionally, extra states are found to appear in the density of states upon doping, which can prove useful for adjusting the conductance of a doped WS<sub>2</sub>-based memristor as required. Finally, the obtained features are used to outline dopant selection criteria based on the valence electron configuration of dopants. The obtained characteristics and outlined criteria can serve as guidelines for the future design of optimized WS<sub>2</sub>-based memristive devices, possessing lower contact resistance and reduced variation in device performance.

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Publication Details

Journal
RSC Advances
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ra02473k
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.92

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article

Doping-induced performance optimization in monolayer WS2 memristor: reduced variability and contact resistance

Tanshia Tahreen Tanisha, Md. Kawsar Alam, Orchi Hassan
4 citations
RSC Advances
Advanced Memory and Neural Computing
2.92
article

Doping-induced performance optimization in monolayer WS2 memristor: reduced variability and contact resistance

Tanshia Tahreen Tanisha, Md. Kawsar Alam, Orchi Hassan
article en
4 citations

Abstract

The memristor is a cornerstone for developing novel non-volatile memory devices that enable brain-like efficient processing and storage capabilities. Two-dimensional transition metal dichalcogenide (TMDC)-based memristors are gaining increasing attention due to the advantages they present over their bulk counterparts. In this work, we employed first-principles calculations to demonstrate that dopants play a significant role in reducing the cycle-to-cycle variability and in lowering the contact resistance in monolayer WS<sub>2</sub>-based memristor. The possibility of reduced cycle-to-cycle variability is reflected by the attractive nature of the calculated interaction energy between dopant metal atoms and a sulphur monovacancy in the WS<sub>2</sub> monolayer. The potential for reduced contact resistance is evident from the reduced tunneling barrier heights and increased tunneling probabilities at the electrode/WS<sub>2</sub> interface upon doping. Additionally, extra states are found to appear in the density of states upon doping, which can prove useful for adjusting the conductance of a doped WS<sub>2</sub>-based memristor as required. Finally, the obtained features are used to outline dopant selection criteria based on the valence electron configuration of dopants. The obtained characteristics and outlined criteria can serve as guidelines for the future design of optimized WS<sub>2</sub>-based memristive devices, possessing lower contact resistance and reduced variation in device performance.

RSC AdvancesVol. 15(32)
Bangladesh University of Engineering and Technology (BD)
University of Engineering and Technology, Lahore, Bangladesh University of Engineering and Technology, Division of Materials Research
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
2.92
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