Enhanced non-volatile resistive switching performance through ion-assisted magnetron sputtering of TiN bottom electrodes
Abstract Emerging non-volatile memristor-based devices with resistive switching (RS) materials are being widely researched as promising contenders for the next generation of data storage and neuromorphic technologies. Titanium nitride (TiN x ) is a common industry-friendly electrode system for RS; however, the precise TiN x properties required for optimum RS performance is still lacking. Herein, using ion-assisted DC magnetron sputtering, we demonstrate the key importance not only of engineering the TiN x bottom electrodes to be dense, smooth, and conductive, but also understoichiometric in N. With these properties, RS in HfO 2 -based memristive devices is shown to be optimised for TiN 0.96 . These devices have switching voltages ≤ ±1 V with promising device-to-device uniformity, endurance, memory window of ~40, and multiple non-volatile intermediate conductance levels. This study highlights the importance of precise tuning of TiN x bottom electrodes to achieve robust performance of oxide resistive switching materials.
Authors
- Andrew J. Flewitt (ORCID: https://orcid.org/0000-0003-4204-4960)
- P. Polcik (ORCID: https://orcid.org/0000-0003-3111-0029)
- Benson Kunhung Tsai
- Judith L. MacManus‐Driscoll (ORCID: https://orcid.org/0000-0003-4987-6620)
- Abhijeet Choudhury (ORCID: https://orcid.org/0000-0002-8484-6083)
- Babak Bakhit (ORCID: https://orcid.org/0000-0002-3083-7536)
- S. Kolozsvári (ORCID: https://orcid.org/0000-0002-3397-7681)
- Markus Hellenbrand (ORCID: https://orcid.org/0000-0002-5811-5228)
- Haiyan Wang (ORCID: https://orcid.org/0000-0002-7397-1209)
Institutions
- Linköping University (SE)
- Purdue University West Lafayette (US)
- University of Cambridge (GB)
- Thinfilm (Sweden) (SE)
- Plansee (Germany) (DE)
Publication Details
- Journal
- Communications Materials
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1038/s43246-025-00798-z
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- Vetenskapsrådet