Synaptic plasticity of ZnO memristive structures for neuromorphic systems of AI

The work shows the results of multilevel resistive switching, as well as synaptic plasticity of TiN/ZnO/TiN memristive structures. Potentiation and depression at 200 voltage pulses with an amplitude of 1.8 V were demonstrated. It was shown that fabricated TiN/ZnO/TiN memristive structures demonstrate multilevel resistive switching with 5 resistive states and resistance ratio HRS/LRS at 1062 ± 35. Five resistive states were maintained for 10 000 s of operation time. Also, spike-timing-dependent plasticity with the asymmetric Hebbian rule at the pulse interval from -9 s to 9 s was demonstrated. The obtained results can be used for hardware implementation of neural networks, neuromorphic structures of robotic complexes, prostheses, and artificial intelligence systems.

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Publication Details

Published
2025-08-25
DOI
https://doi.org/10.1109/cnn67635.2025.11177483
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Synaptic plasticity of ZnO memristive structures for neuromorphic systems of AI

Daniel Rodrı́guez, A. Yu. Popova, V. A. Smirnov, Zakhar Vakulov et al.
Advanced Memory and Neural Computing
article

Synaptic plasticity of ZnO memristive structures for neuromorphic systems of AI

Daniel Rodrı́guez, A. Yu. Popova, V. A. Smirnov, Zakhar Vakulov, R V Tominov, I. S. Ugryumov
article en

Abstract

The work shows the results of multilevel resistive switching, as well as synaptic plasticity of TiN/ZnO/TiN memristive structures. Potentiation and depression at 200 voltage pulses with an amplitude of 1.8 V were demonstrated. It was shown that fabricated TiN/ZnO/TiN memristive structures demonstrate multilevel resistive switching with 5 resistive states and resistance ratio HRS/LRS at 1062 ± 35. Five resistive states were maintained for 10 000 s of operation time. Also, spike-timing-dependent plasticity with the asymmetric Hebbian rule at the pulse interval from -9 s to 9 s was demonstrated. The obtained results can be used for hardware implementation of neural networks, neuromorphic structures of robotic complexes, prostheses, and artificial intelligence systems.

Southern Federal University (RU)
Openalex Percentile: Top 73%
Advanced Memory and Neural Computing
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