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Published on: April 27, 2015
Enhanced Long-Term In-Sensing Memory in ZnO Nanoparticle-Based Optoelectronic Synaptic Devices Through Thermal
Dabin Jeon1, Seung Hun Lee1, Sung-Nam Lee1,2
1Department of IT Semiconductor Convergence Engineering, Tech University of Korea, Siheung 15073, Republic of Korea.
Abstract:
Two-terminal optoelectronic synaptic devices based on ZnO nanoparticles (NPs) were fabricated to investigate the effects of thermal annealing control (200 °C-500 °C) in nitrogen and oxygen atmospheres on surface morphology, optical response, and synaptic functionality. Atomic force microscopy (AFM) analysis revealed improved grain growth and reduced surface roughness. At the same time, UV-visible spectroscopy and photoluminescence confirmed a blue shift in the absorption edge and enhanced near-band-edge emission, particularly in nitrogen-annealed devices due to increased oxygen vacancies. X-ray photoelectron spectroscopy (XPS) analysis of the O 1s spectra confirmed that oxygen vacancies were more pronounced in nitrogen-annealed devices than in oxygen-annealed ones at 500 °C. Optical resistive switching was observed, where 365 nm ultraviolet (UV) irradiation induced a transition from a high-resistance state (HRS) to a low-resistance state (LRS), attributed to electron-hole pair generation and oxygen desorption. The electrical reset process, achieved by applying -1.0 V to -5.0 V, restored the initial HRS, demonstrating stable switching behavior. Nitrogen-annealed devices with higher oxygen vacancies exhibited superior synaptic performance, including higher excitatory postsynaptic currents, stronger paired-pulse facilitation, and extended persistent photoconductivity (PPC) duration, enabling long-term memory retention. By systematically varying UV exposure time, intensity, pulse number, and frequency, ZnO NPs-based devices demonstrated the transition from short-term to long-term memory, mimicking biological synaptic behavior. Learning and forgetting simulations showed faster learning and slower decay in nitrogen-annealed devices, emphasizing their potential for next-generation neuromorphic computing and energy-efficient artificial synapses.

