Reservoir computing and advanced synaptic plasticity of sputter-deposited ZnO memristors with controllable threshold and nonvolatile switching behavior

This study presents an ITO/ZnO/ITO/Si memristor fabricated via reactive sputtering for use in advanced analog synaptic plasticity and reservoir computing (RC) systems. The proposed device exhibited stable threshold and nonvolatile switching characteristics by effectively controlling the current comp...

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Bibliographic Details
Published in:The Journal of chemical physics 2024-12, Vol.161 (22)
Main Authors: Ismail, Muhammad, Seo, Euncho, Rasheed, Maria, Park, Yongjin, Mahata, Chandreswar, Kim, Sungjun
Format: Article
Language:English
Subjects:
Argon
Artificial neural networks
Binary codes
Computation
Controllability
Data storage
Filaments
Handwriting recognition
Image processing
Memristors
Oxygen
Zinc oxide
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Summary:This study presents an ITO/ZnO/ITO/Si memristor fabricated via reactive sputtering for use in advanced analog synaptic plasticity and reservoir computing (RC) systems. The proposed device exhibited stable threshold and nonvolatile switching characteristics by effectively controlling the current compliance (ICC) limit. Multilevel data storage was achieved through controlled multistate switching via reset-stop voltage and ICC. X-ray diffraction analysis confirmed the formation of a polycrystalline ZnO film with a 12:8 oxygen-to-argon ratio, which facilitated the generation of oxygen-vacancy conductive filaments. The memristor effectively replicated key synaptic characteristics such as long-term potentiation, long-term depression, spike-amplitude/width-dependent plasticity, spike-rate-dependent plasticity, and the transition from short-term to long-term memory. The RC system processed binary 4-bit codes and recognized different digits, achieving 98.84% accuracy in handwritten digit recognition using a convolutional neural network simulation, highlighting its potential for efficient image processing applications.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0243669