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    Home»Nanotechnology»Interface Engineered V2O5-based Flexible Memristors towards High-Performance Brain-Inspired Neuromorphic Computing
    Nanotechnology

    Interface Engineered V2O5-based Flexible Memristors towards High-Performance Brain-Inspired Neuromorphic Computing

    big tee tech hubBy big tee tech hubDecember 14, 2025022 Mins Read
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    Interface Engineered V2O5-based Flexible Memristors towards High-Performance Brain-Inspired Neuromorphic Computing
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    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, D1(Nb/V2O5/Ni) and D2(Nb/NbOx/V2O5/Ni). Device D1 exhibits abrupt SET and gradual RESET switching, while D2 demonstrates fully gradual resistive switching (GRS), highly desirable for analog synaptic behavior. Mechanistically, D1 is primarily governed by oxygen vacancies, whereas D2 benefits from the synergistic interplay between oxygen vacancies and interfacial NbOx/NiO layers, confirmed by XPS depth profiling. These interfacial layers significantly enhance D2’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 D2 outperforming D1 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, D2 also mimics complex neural dynamics, including spike time-dependent and rate-dependent plasticity. Simulation of D2’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 V2O5-based devices for high-performance neuromorphic computing.


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