Photoactive Metal–Organic Framework Heterojunctions for Optoelectronic Synapse and Neuromorphic Computing

ABSTRACT Metal–organic frameworks combine the structural tunability of organic materials with the periodic order of inorganic lattices, making them attractive candidates for neuromorphic devices. However, most MOF‐based synaptic devices reported to date have been limited to electrically driven memristors because conventional MOFs generally suffer from poor conductivity and inadequate film quality. Here, we report the in situ preparation of photoactive ZnO/Zn 3 (HHTP) 2 heterojunction that enables optoelectronic synaptic plasticity and neuromorphic computing. Two‐dimensional Zn 3 (HHTP) 2 framework is synthesized via the coordination reaction between 2,3,6,7,10,11‐hexahydroxytriphenylene and Zn 2+ derived from sacrificial ZnO nanorods, yielding a porous conductive MOF integrated directly with ZnO. The resulting ZnO/ZnHHTP heterojunction forms type‐II band alignment, facilitating efficient separation and transport of photo‐generated carriers. Combined with defect‐assisted trapping and relaxation processes, the device emulates key synaptic functions, including paired‐pulse facilitation, spike dependent plasticity, and transition from short‐term plasticity to long‐term plasticity. Benefiting from dual‐wavelength optical responses under 445 and 520 nm illumination, the device also realizes all‐optical “AND” and “OR” logic operations. Furthermore, when integrated into a reservoir computing framework, the nonlinear transient responses of the heterojunction enable handwritten digit recognition with an accuracy of ∼95% on the MNIST dataset. This work establishes conductive MOF‐based heterojunctions as promising platforms for neuromorphic information processing.

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Journal
Small
Published
2026-09-16
DOI
https://doi.org/10.1002/smll.75818
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Photoactive Metal–Organic Framework Heterojunctions for Optoelectronic Synapse and Neuromorphic Computing

Yan Cheng, L. Zhang, Hechun Lin, Chunli Jiang et al.
Small
Advanced Memory and Neural Computing
article

Photoactive Metal–Organic Framework Heterojunctions for Optoelectronic Synapse and Neuromorphic Computing

Yan Cheng, L. Zhang, Hechun Lin, Chunli Jiang, Hui Peng, Shuaifei Mao, Chunhua Luo, Zhen Zhang
article en

Abstract

ABSTRACT Metal–organic frameworks combine the structural tunability of organic materials with the periodic order of inorganic lattices, making them attractive candidates for neuromorphic devices. However, most MOF‐based synaptic devices reported to date have been limited to electrically driven memristors because conventional MOFs generally suffer from poor conductivity and inadequate film quality. Here, we report the in situ preparation of photoactive ZnO/Zn 3 (HHTP) 2 heterojunction that enables optoelectronic synaptic plasticity and neuromorphic computing. Two‐dimensional Zn 3 (HHTP) 2 framework is synthesized via the coordination reaction between 2,3,6,7,10,11‐hexahydroxytriphenylene and Zn 2+ derived from sacrificial ZnO nanorods, yielding a porous conductive MOF integrated directly with ZnO. The resulting ZnO/ZnHHTP heterojunction forms type‐II band alignment, facilitating efficient separation and transport of photo‐generated carriers. Combined with defect‐assisted trapping and relaxation processes, the device emulates key synaptic functions, including paired‐pulse facilitation, spike dependent plasticity, and transition from short‐term plasticity to long‐term plasticity. Benefiting from dual‐wavelength optical responses under 445 and 520 nm illumination, the device also realizes all‐optical “AND” and “OR” logic operations. Furthermore, when integrated into a reservoir computing framework, the nonlinear transient responses of the heterojunction enable handwritten digit recognition with an accuracy of ∼95% on the MNIST dataset. This work establishes conductive MOF‐based heterojunctions as promising platforms for neuromorphic information processing.

Small
Army Medical University (CN), Shanxi University (CN), East China Normal University (CN)
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
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