Hydrothermally synthesized and exfoliated WS <sub>2</sub> –PEO nanocomposite based non-volatile resistive devices for energy-efficient neuromorphic applications
This work presents a facile, scalable nanocomposite-based resistive memory device incorporating a 2D hybrid of hydrothermally synthesized and exfoliated tungsten disulfide (E-WS<sub>2</sub>) nanosheets embedded in a poly (ethylene oxide) (PEO) matrix for energy efficient neuromorphic applications. WS<sub>2</sub>was synthesized via a simple, cost-effective hydrothermal method and subsequently exfoliated via liquid phase exfoliation to obtain few-layer nanosheets with improved surface uniformity and reduced defect density. These nanosheets were integrated into the active layer of an ITO/E-WS₂+ PEO/Cu device fabricated via spin coating and thermal evaporation. The device exhibits reliable bipolar resistive switching with low SET voltages, a high ON/OFF current ratio (∼10⁴), excellent retention (>450 s), and endurance over 70 cycles. The transport mechanism is governed by Ohmic conduction at low voltages, followed by space charge limited current (SCLC) and trap-controlled SCLC (TC-SCLC) mechanisms near-threshold voltages. Energy band analysis indicates that charge trapping and de-trapping at the WS<sub>2</sub>/PEO interface plays a critical role in the switching process. Compared to bulk WS<sub>2</sub>, exfoliated WS<sub>2</sub>offers enhanced interfacial contact, lower resistance pathways, and reduced variability in switching, resulting in improved device performance and stability. It also shows more analog like behavior. Sulfur vacancies in E-WS₂ assist in forming conductive filaments, while the PEO matrix enhances ionic mobility and switching behavior. This work offers a scalable, environmentally benign approach to fabricating 2D material-based resistive memory, establishing solution-processed E-WS₂ nanocomposites as strong candidates for next-generation, scalable, energy-efficient non-volatile memory and neuromorphic technologies.
Authors
- Franco Mayanglambam (ORCID: https://orcid.org/0000-0002-1592-7699)
- Nipom Sekhar Das (ORCID: https://orcid.org/0000-0001-6434-8223)
- Tanmay Dutta (ORCID: https://orcid.org/0000-0002-6637-5185)
- S.M. Das (ORCID: https://orcid.org/0009-0002-5600-0918)
Institutions
- Indian Institute of Technology Guwahati (IN)
- Sri Konda Laxman Telangana State Horticultural University (IN)
- Dr. Reddy's Laboratories (India) (IN)
Publication Details
- Journal
- Nanotechnology
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1088/1361-6528/ae291e
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00