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.
Authors
- Tanshia Tahreen Tanisha (ORCID: https://orcid.org/0009-0000-2369-6440)
- Md. Kawsar Alam (ORCID: https://orcid.org/0000-0003-0467-3248)
- Orchi Hassan (ORCID: https://orcid.org/0000-0003-4886-1419)
Institutions
- Bangladesh University of Engineering and Technology (BD)
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
Funders
- University of Engineering and Technology, Lahore
- Bangladesh University of Engineering and Technology
- Division of Materials Research