Synaptic Plasticity and Memory Retention in ZnO–CNT Nanocomposite Optoelectronic Synaptic Devices

This study presents the fabrication and characterization of ZnO-CNT composite-based optoelectronic synaptic devices via a sol-gel process. By incorporating various concentrations of CNTs (0-2.0 wt%) into ZnO thin films, we investigated their effects on synaptic behaviors under ultraviolet (UV) stimulation. The CNT addition enhanced the electrical and optical performance by forming a p-n heterojunction with ZnO, which promoted charge separation and suppressed recombination. As a result, the 1.5 wt% CNT device exhibited the highest excitatory postsynaptic current (EPSC), improved paired-pulse facilitation, and prolonged memory retention. Learning-forgetting cycles revealed that repeated stimulation reduced the number of pulses required for relearning while extending the forgetting time, mimicking biological memory reinforcement. Energy consumption per pulse was estimated at 16.34 nJ, suggesting potential for low-power neuromorphic applications. A 3 × 3 device array was also employed for visual memory simulation, showing spatially controllable and stable memory states depending on CNT content. To support these findings, structural and optical analyses were conducted using scanning electron microscopy (SEM), UV-visible absorption spectroscopy, photoluminescence (PL) spectroscopy, and Raman spectroscopy. These findings demonstrate that the synaptic characteristics of ZnO-based devices can be finely tuned through CNT incorporation, providing a promising pathway for the development of energy-efficient and adaptive optoelectronic neuromorphic systems.

Authors

Institutions

Publication Details

Journal
Materials
Published
2025-05-15
DOI
https://doi.org/10.3390/ma18102293
Citations
8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.84

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Synaptic Plasticity and Memory Retention in ZnO–CNT Nanocomposite Optoelectronic Synaptic Devices

Dabin Jeon, Sung‐Nam Lee, Seung Hun Lee
8 citations
Materials
Advanced Memory and Neural Computing
5.84
article

Synaptic Plasticity and Memory Retention in ZnO–CNT Nanocomposite Optoelectronic Synaptic Devices

Dabin Jeon, Sung‐Nam Lee, Seung Hun Lee
article en
8 citations

Abstract

This study presents the fabrication and characterization of ZnO-CNT composite-based optoelectronic synaptic devices via a sol-gel process. By incorporating various concentrations of CNTs (0-2.0 wt%) into ZnO thin films, we investigated their effects on synaptic behaviors under ultraviolet (UV) stimulation. The CNT addition enhanced the electrical and optical performance by forming a p-n heterojunction with ZnO, which promoted charge separation and suppressed recombination. As a result, the 1.5 wt% CNT device exhibited the highest excitatory postsynaptic current (EPSC), improved paired-pulse facilitation, and prolonged memory retention. Learning-forgetting cycles revealed that repeated stimulation reduced the number of pulses required for relearning while extending the forgetting time, mimicking biological memory reinforcement. Energy consumption per pulse was estimated at 16.34 nJ, suggesting potential for low-power neuromorphic applications. A 3 × 3 device array was also employed for visual memory simulation, showing spatially controllable and stable memory states depending on CNT content. To support these findings, structural and optical analyses were conducted using scanning electron microscopy (SEM), UV-visible absorption spectroscopy, photoluminescence (PL) spectroscopy, and Raman spectroscopy. These findings demonstrate that the synaptic characteristics of ZnO-based devices can be finely tuned through CNT incorporation, providing a promising pathway for the development of energy-efficient and adaptive optoelectronic neuromorphic systems.

MaterialsVol. 18(10)
Korea University of Technology and Education (KR)
Ministry of Science and ICT, South Korea
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.