Tuning resistive switching in ZnO and TiO2 nanostructures with cobalt doping

Abstract Resistive Random Access Memories (RRAMs) traditionally utilize a metal/insulator/metal architecture. This study introduces an innovative configuration employing metal/oxide-diluted magnetic semiconductors (O-DMS)/metal on flexible substrate, leveraging the enhanced performance of magnetic control in resistive switching. We investigated the structural, morphological, magnetic, and electrical properties of cobalt-doped ZnO and TiO 2 thin films, synthesized via DC magnetron sputtering. XRD measurements stablish the presence of Co 3 O 4 phases in the samples of Co-doped ZnO thin films with substrate temperature (Ts) of 423 K, while Raman spectra of Co-doped TiO 2 thin film not evidencing the formation of the Co–O binary phases associated to the low substrate temperature (Ts = 293 K). High-resolution SEM and AFM analyses revealed the formation of small grains on the film surfaces, indicative of the growth mechanisms. When Co target power was increased between 20 and 40 W, the grain size increased from 158.89 ± 4.76 nm to 460.97 ± 13.82 nm. Electrical and magnetic characterizations demonstrated contributions from lattice free electrons, generated by oxygen vacancies, and randomly distributed Co ions within the oxide semiconductor matrix, influencing the SET and RESET states. Comparative analysis of ZnO and TiO 2 matrices indicated reduced energy consumption and increased storage capacity, attributed to the modulation of high and low resistive states by magnetic ions within the semiconductor matrix, associated to change between low resistive state (LRS) and HRS occurs (~ 1–3 V).

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Publication Details

Journal
Journal of Materials Science Materials in Electronics
Published
2025-03-01
DOI
https://doi.org/10.1007/s10854-025-14348-3
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Tuning resistive switching in ZnO and TiO2 nanostructures with cobalt doping

A. Dussán, Cristian L. Terán, Heiddy P. Quiroz, Jorge A. Calderón
1 citations
Journal of Materials Science Materials in Electronics
Advanced Memory and Neural Computing
0.73
article

Tuning resistive switching in ZnO and TiO2 nanostructures with cobalt doping

A. Dussán, Cristian L. Terán, Heiddy P. Quiroz, Jorge A. Calderón
article en
1 citations

Abstract

Abstract Resistive Random Access Memories (RRAMs) traditionally utilize a metal/insulator/metal architecture. This study introduces an innovative configuration employing metal/oxide-diluted magnetic semiconductors (O-DMS)/metal on flexible substrate, leveraging the enhanced performance of magnetic control in resistive switching. We investigated the structural, morphological, magnetic, and electrical properties of cobalt-doped ZnO and TiO 2 thin films, synthesized via DC magnetron sputtering. XRD measurements stablish the presence of Co 3 O 4 phases in the samples of Co-doped ZnO thin films with substrate temperature (Ts) of 423 K, while Raman spectra of Co-doped TiO 2 thin film not evidencing the formation of the Co–O binary phases associated to the low substrate temperature (Ts = 293 K). High-resolution SEM and AFM analyses revealed the formation of small grains on the film surfaces, indicative of the growth mechanisms. When Co target power was increased between 20 and 40 W, the grain size increased from 158.89 ± 4.76 nm to 460.97 ± 13.82 nm. Electrical and magnetic characterizations demonstrated contributions from lattice free electrons, generated by oxygen vacancies, and randomly distributed Co ions within the oxide semiconductor matrix, influencing the SET and RESET states. Comparative analysis of ZnO and TiO 2 matrices indicated reduced energy consumption and increased storage capacity, attributed to the modulation of high and low resistive states by magnetic ions within the semiconductor matrix, associated to change between low resistive state (LRS) and HRS occurs (~ 1–3 V).

Journal of Materials Science Materials in ElectronicsVol. 36(7)
Pedagogical and Technological University of Colombia (CO), Universidad Nacional de Colombia (CO)
Universidad Nacional de Colombia
Openalex Percentile: Top 31%
Advanced Memory and Neural Computing
0.73
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