Electroresistance mechanism of memristive Cu2S thin films
Cu 2 S has been widely used as the resistive-layer material of memristors with the advantage of low-power consumption since the Cu-ions have high mobility and migrate quickly under the applied electric field or current. However, the resistance switching mechanisms remain unclear as direct experimental evidence is still lacking. Here, we prepare several prototypes of metal-Cu 2 S-metal memristors, where the resistive layer is a thin film of polycrystalline Cu 2 S fabricated by a simple magnetron sputtering process. By fitting the resistive curves, space charge limited conduction (SCLC) and electrochemical metallization (ECM) are identified in devices using different electrodes and compliance currents. The microstructural evolutions of the devices in their working conditions are then studied by in-situ transmission electron microscopy (TEM) to directly observe the formation and rupture of the electrically conductive pathways. We find the resistance switch at small current compliance is caused by the phase transformation between γ (L)-Cu 2 S phase and the Cu-deficiency γ (D)-Cu 2-x S phase (corresponding to SCLC mechanism). Meanwhile, the origin of resistance change at a large current compliance is the formation of metal Cu filaments (corresponding to ECM mechanism). The study reveals the underlying resistive mechanisms of Cu 2 S, providing insights for its potential applications as the resistive material in high-performance memristors.
Authors
- Jinsong Wu (ORCID: https://orcid.org/0000-0002-7305-7927)
- Zhengzhou Wang (ORCID: https://orcid.org/0000-0001-8579-9218)
- Yuan Li (ORCID: https://orcid.org/0000-0002-8336-4840)
- Qin Li (ORCID: https://orcid.org/0000-0001-5195-3985)
- Zhen Zhu
- Ruifeng Dong (ORCID: https://orcid.org/0000-0002-3722-1742)
- Meiyan Wang
Institutions
- Wuhan University of Technology (CN)
Publication Details
- Journal
- Fundamental Research
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1016/j.fmre.2025.02.021
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73