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Reset-First and Multibit-Level Resistive-Switching Behavior of Lanthanum Nickel Oxide (LaNiO(3−x)) Thin Films

The resistive random-access memory (RRAM) with multi-level storage capability has been considered one of the most promising emerging devices to mimic synaptic behavior and accelerate analog computations. In this study, we investigated the reset-first bipolar resistive switching (RS) and multi-level...

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Detalles Bibliográficos
Autores principales: Kim, Daewoo, Lee, Jeongwoo, Kim, Jaeyeon, Sohn, Hyunchul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384036/
https://www.ncbi.nlm.nih.gov/pubmed/37512267
http://dx.doi.org/10.3390/ma16144992
Descripción
Sumario:The resistive random-access memory (RRAM) with multi-level storage capability has been considered one of the most promising emerging devices to mimic synaptic behavior and accelerate analog computations. In this study, we investigated the reset-first bipolar resistive switching (RS) and multi-level characteristics of a LaNiO(3−x) thin film deposited using a reactive magnetron co-sputtering method. Polycrystalline phases of LaNiO(3) (LNO), without La(2)O(3) and NiO phases, were observed at similar fractions of Ni and La at a constant partial pressure of oxygen. The relative chemical proportions of Ni(3+) and Ni(2+) ions in LaNiO(3−x) indicated that it was an oxygen-deficient LaNiO(3−x) thin film, exhibiting RS behavior, compared to LNO without Ni(2+) ions. The TiN/LaNiO(3−x)/Pt devices exhibited gradual resistance changes under various DC/AC voltage sweeps and consecutive pulse modes. The nonlinearity values of the conductance, measured via constant-pulse programming, were 0.15 for potentiation and 0.35 for depression, indicating the potential of the as-fabricated devices as analog computing devices. The LaNiO(3−x)-based device could reach multi-level states without an electroforming step and is a promising candidate for state-of-the-art RS memory and synaptic devices for neuromorphic computing.