Optically Tunable Synaptic Plasticity and Memory Emulation in Au‐Nanoparticle Enhanced HfTiO <sub>x</sub> /Al <sub>2</sub> O <sub>3</sub> ‐Based Photonic Memristors

Abstract The optically stimulated synaptic device incorporates gold nanoparticles (Au‐NPs) embedded within an atomic‐layer‐deposited HfTiO x /Al 2 O 3 bilayer, which enables the photonic modulation of synaptic plasticity. The HfTiO x /Au‐NP interface enhances visible light absorption and utilization efficiency by generating ionized oxygen vacancies through the neutral oxygen vacancy sites, thereby modulating the device conductance states. This architecture effectively mimics key biological synaptic functions, including paired‐pulse facilitation (PPF), memory transitions from short‐term to long‐term memory (STM to LTM), and spike‐rate‐dependent plasticity (SRDP). The device further demonstrates optical logic operations and Pavlovian associative learning under dual‐wavelength light stimulation (405 and 450 nm). The intensity‐dependent generation of photocarriers and their nonlinear interaction with oxygen vacancies enable robust synaptic behavior, facilitating the emulation of human visual perception in a 4 × 4 optoelectronic synapse array with short‐term memory capabilities. Moreover, the wavelength‐ and sequence‐dependent synaptic responses can be finely controlled for the design of light‐programmable reservoir computing systems. These results demonstrate the potential of the HfTiO x /Au‐NP/Al 2 O 3 switching layer as a promising platform for efficient neuromorphic computing and vision‐based information processing using integrated optoelectronic synapses.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-07-21
DOI
https://doi.org/10.1002/adfm.202510663
Citations
9
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
6.57

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article

Optically Tunable Synaptic Plasticity and Memory Emulation in Au‐Nanoparticle Enhanced HfTiO x /Al 2 O 3 ‐Based Photonic Memristors

Chandreswar Mahata, Muhammad Ismail, Sungjun Kim, Hyungjin Kim
9 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
6.57
article

Optically Tunable Synaptic Plasticity and Memory Emulation in Au‐Nanoparticle Enhanced HfTiO x /Al 2 O 3 ‐Based Photonic Memristors

Chandreswar Mahata, Muhammad Ismail, Sungjun Kim, Hyungjin Kim
article en
9 citations

Abstract

Abstract The optically stimulated synaptic device incorporates gold nanoparticles (Au‐NPs) embedded within an atomic‐layer‐deposited HfTiO x /Al 2 O 3 bilayer, which enables the photonic modulation of synaptic plasticity. The HfTiO x /Au‐NP interface enhances visible light absorption and utilization efficiency by generating ionized oxygen vacancies through the neutral oxygen vacancy sites, thereby modulating the device conductance states. This architecture effectively mimics key biological synaptic functions, including paired‐pulse facilitation (PPF), memory transitions from short‐term to long‐term memory (STM to LTM), and spike‐rate‐dependent plasticity (SRDP). The device further demonstrates optical logic operations and Pavlovian associative learning under dual‐wavelength light stimulation (405 and 450 nm). The intensity‐dependent generation of photocarriers and their nonlinear interaction with oxygen vacancies enable robust synaptic behavior, facilitating the emulation of human visual perception in a 4 × 4 optoelectronic synapse array with short‐term memory capabilities. Moreover, the wavelength‐ and sequence‐dependent synaptic responses can be finely controlled for the design of light‐programmable reservoir computing systems. These results demonstrate the potential of the HfTiO x /Au‐NP/Al 2 O 3 switching layer as a promising platform for efficient neuromorphic computing and vision‐based information processing using integrated optoelectronic synapses.

Advanced Functional MaterialsVol. 36(3)
Dongguk University (KR), Hanyang University (KR)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
6.57
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