Enhanced analog switching and neuromorphic performance of ZnO-based memristors with indium tin oxide electrodes for high-accuracy pattern recognition.

Autor:	Ismail M; Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea., Rasheed M; Department of Advanced Battery Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea., Park Y; Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea., Lee S; Department of Advanced Battery Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea., Mahata C; Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea., Shim W; Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea., Kim S; Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Jazyk:	angličtina
Zdroj:	The Journal of chemical physics [J Chem Phys] 2024 Oct 07; Vol. 161 (13).
DOI:	10.1063/5.0233031
Abstrakt:	This study systematically investigates analog switching and neuromorphic characteristics in a ZnO-based memristor by varying the anodic top electrode (TE) materials [indium tin oxide (ITO), Ti, and Ta]. Compared with the TE materials (Ti and Ta), memristive devices with TEs made of ITO exhibit dual volatile and nonvolatile switching behavior and multistate switching characteristics assessed based on reset-stop voltage and current compliance (ICC) responses. The polycrystalline structure of the ZnO functional layer sandwiched between ITO electrodes was confirmed by high-resolution transmission electron microscopy analysis. The current transport mechanism in the ZnO-based memristor was dominated by Schottky emission, with the Schottky barrier height modulated from 0.26 to 0.4 V by varying the reset-stop voltage under different ICC conditions. The long-term potentiation and long-term depression synaptic characteristics were successfully mimicked by modulating the pulse amplitudes. Furthermore, a 90.84% accuracy was achieved using a convolutional neural network architecture for Modified National Institute of Standards and Technology pattern categorization, as demonstrated by the confusion matrix. The results demonstrated that the ITO/ZnO/ITO/Si memristor device holds promise for high-performance electronic applications and effective ITO electrode modeling. (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)
Databáze:	MEDLINE
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