Controlled oxygen vacancy electrode reservoir for robust WO3-based memory devices
Abstract Oxide-based memristors are promising candidates as neuromorphic hardware in energy-efficient edge computing applications for the Internet of Things (IoT). However, achieving simultaneously optimized memristive and synaptic performance with low-temperature fabrication, compatible with complementary metal-oxide-semiconductor (CMOS) processes, remains a challenge. Here, we demonstrate a dual-function electrode selection strategy—controlled oxygen vacancy electrode reservoir—implemented in a simple, low-temperature-fabricated indium tin oxide (ITO)/WO 3 /TiN thin film system. We systematically vary the oxygen stoichiometry in the ITO top electrode, which serves not only as an electrical contact, but also as a dynamic oxygen reservoir. A high oxygen-vacancy concentration in the ITO enables optimized memristive performance of low electroforming and operation voltages, and robust memristive and synaptic endurance. This electrode-focused defect engineering approach offers a versatile route to advanced memristor design, enabling many applications in IoT and neuromorphic systems.
Authors
- Caterina Ducati (ORCID: https://orcid.org/0000-0003-3366-6442)
- Atif Jan (ORCID: https://orcid.org/0000-0001-5746-3450)
- Bipin Rajendran (ORCID: https://orcid.org/0000-0002-2960-6909)
- Xinjuan Li (ORCID: https://orcid.org/0000-0003-3086-9287)
- Babak Bakhit (ORCID: https://orcid.org/0000-0002-3083-7536)
- Giuliana Di Martino (ORCID: https://orcid.org/0000-0001-5766-8384)
- Markus Hellenbrand (ORCID: https://orcid.org/0000-0002-5811-5228)
- Abin Varghese (ORCID: https://orcid.org/0000-0002-6241-9080)
- Jiahao Lu
- Judith Louise MacManus-Driscoll
- Yi-Xuan Liu
- Ziyi Yuan
Institutions
- Linköping University (SE)
- King's College London (GB)
- University of Cambridge (GB)
- Thinfilm (Sweden) (SE)
Publication Details
- Journal
- Communications Materials
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1038/s43246-026-01143-8
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- Royal Academy of Engineering
- Vetenskapsrådet
- Horizon 2020 Framework Programme
- Engineering and Physical Sciences Research Council