Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

Abstract Organic electrochemical transistors (OECTs) are promising for neuromorphic architectures as they can generate multiple electrical states through the control of ion transport. However, conventional OECTs face limitations in mimicking a fully functional biological synapse due to their inability to achieve long‐term plasticity. In this study, a metal‐organic framework (MOF)‐enhanced OECT (MOECT) is fabricated by introducing MOF into the ion‐organic semiconductor (OSC) layer. MOFs are synthesized using the layer‐by‐layer (LBL) method, and additional cross‐linked OSC is introduced to prevent damage to the semiconductor layers during synthesis. The synthesized MOF layers hinder the rediffusion of ions in OECT, allowing ions to remain in the OSC for an extended period. In this study, the MOECT showed a change in current depending on the doping level, recording a current state 4.4 × 10 7 times higher than that of pristine OECT. Ultimately, the developed MOECTs are applied as synaptic transistors. MOECTs show 14% higher excitatory postsynaptic current (EPSC) after 130 s compared to pristine OECT, thereby strengthening the long‐term plasticity characteristics of neuromorphic devices. This method enhances the performance of synaptic transistors by introducing MOF, offering various possibilities through the selection of different MOF structures and ions, indicating it is a methodological approach with high potential.

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Publication Details

Journal
Advanced Materials Technologies
Published
2024-11-12
DOI
https://doi.org/10.1002/admt.202401316
Citations
10
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.09

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article

Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

Yousang Won, Joon Hak Oh, Eun Kwang Lee, Sunmin Park
10 citations
Advanced Materials Technologies
Advanced Memory and Neural Computing
2.09
article

Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

Yousang Won, Joon Hak Oh, Eun Kwang Lee, Sunmin Park
article en
10 citations

Abstract

Abstract Organic electrochemical transistors (OECTs) are promising for neuromorphic architectures as they can generate multiple electrical states through the control of ion transport. However, conventional OECTs face limitations in mimicking a fully functional biological synapse due to their inability to achieve long‐term plasticity. In this study, a metal‐organic framework (MOF)‐enhanced OECT (MOECT) is fabricated by introducing MOF into the ion‐organic semiconductor (OSC) layer. MOFs are synthesized using the layer‐by‐layer (LBL) method, and additional cross‐linked OSC is introduced to prevent damage to the semiconductor layers during synthesis. The synthesized MOF layers hinder the rediffusion of ions in OECT, allowing ions to remain in the OSC for an extended period. In this study, the MOECT showed a change in current depending on the doping level, recording a current state 4.4 × 10 7 times higher than that of pristine OECT. Ultimately, the developed MOECTs are applied as synaptic transistors. MOECTs show 14% higher excitatory postsynaptic current (EPSC) after 130 s compared to pristine OECT, thereby strengthening the long‐term plasticity characteristics of neuromorphic devices. This method enhances the performance of synaptic transistors by introducing MOF, offering various possibilities through the selection of different MOF structures and ions, indicating it is a methodological approach with high potential.

Advanced Materials TechnologiesVol. 10(6)
Seoul National University (KR), Ulsan National Institute of Science and Technology (KR), Pukyong National University (KR)
National Research Foundation, Seoul National University, Ministry of Education, National Research Foundation of Korea, Institute of Engineering Research, Seoul National University
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
2.09
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