Interface engineered V <sub>2</sub> O <sub>5</sub> -based flexible memristors towards high-performance brain-inspired neuromorphic computing
Bio-inspired neuromorphic computing offers a revolutionary approach by replicating brain-like functionalities in next-generation electronics. This study presents two flexible resistive memory devices fabricated using DC magnetron sputtering, D<sub>1</sub>(Nb/V<sub>2</sub>O<sub>5</sub>/Ni) and D<sub>2</sub>(Nb/NbO<sub><i>x</i></sub>/V<sub>2</sub>O<sub>5</sub>/Ni). Device D<sub>1</sub> exhibits abrupt SET and gradual RESET switching, while D<sub>2</sub> demonstrates fully gradual resistive switching (GRS), highly desirable for analog synaptic behavior. Mechanistically, D<sub>1</sub> is primarily governed by oxygen vacancies, whereas D<sub>2</sub> benefits from the synergistic interplay between oxygen vacancies and interfacial NbO<sub><i>x</i></sub>/NiO layers, confirmed by XPS depth profiling. These interfacial layers significantly enhance D<sub>2</sub>'s GRS performance and synaptic fidelity. Both devices exhibit temperature-dependent control of oxygen vacancies, which dynamically increases the memory window, lowering the ON/OFF ratio. Multilevel resistive states are generated in both devices by controlling the compliance current, with D<sub>2</sub> outperforming D<sub>1</sub> by exhibiting a higher memory window (∼552) and exceptional endurance beyond 7000 cycles. Moreover, both devices effectively replicate biological synaptic functions such as LTP and LTD. However, D<sub>2</sub> also mimics complex neural dynamics, including spike time-dependent and rate-dependent plasticity. Simulation of D<sub>2</sub>'s artificial neural network demonstrates ∼86.75% excellent accuracy level, attributed to its linear, symmetric analog weight modulation and multiple conductance states. These results highlight the potential of V<sub>2</sub>O<sub>5</sub>-based devices for high-performance neuromorphic computing.
Authors
- Kumar Kaushlendra (ORCID: https://orcid.org/0000-0001-5244-0743)
- Davinder Kaur (ORCID: https://orcid.org/0000-0002-7293-625X)
Institutions
- Indian Institute of Technology Roorkee (IN)
Publication Details
- Journal
- Nanoscale Horizons
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1039/d5nh00562k
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00