Gate Electrode Engineering for Tunable Synaptic Plasticity in Organic Electrochemical Neuromorphic Devices

ABSTRACT Organic electrochemical transistors (OECTs) are widely studied for neuromorphic hardware, as their volumetric electrochemical doping couples ionic and electronic transport to mimic biological synaptic transmission. Research on OECT‐based artificial synapses has focused predominantly on the organic mixed ionic–electronic conductor channel, while the gate electrode has largely been treated as a passive input terminal. Yet because the gate electrode governs electrochemical injection, retention, and extraction of ions at the gate/electrolyte interface, it offers a direct and underexplored means of controlling synaptic plasticity. This review summarizes recent progress on gate electrode engineering for tuning plasticity in OECT‐based neuromorphic devices, organized around three interconnected axes: gate material chemistry, gate architecture and geometry, and gate dielectric/electrolyte interface engineering. We first outline OECT operating principles, the biological basis of synaptic plasticity, and standard neuromorphic figures of merit. We then examine how gate capacitance, ion selectivity, electrode geometry, and interfacial chemistry govern the transition from short‐ to long‐term plasticity across material classes spanning metals, carbons, conducting polymers, metal–organic frameworks, and emerging composites. Applications including associative learning, spike‐timing‐dependent plasticity, reservoir computing, and hardware neural networks are surveyed, and remaining challenges for scalable device integration are discussed.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-10-06
DOI
https://doi.org/10.1002/adfm.78803
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Gate Electrode Engineering for Tunable Synaptic Plasticity in Organic Electrochemical Neuromorphic Devices

Woojo Kim, Wen‐Ya Lee, Fabrizio Torricelli, Eun Kwang Lee et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Gate Electrode Engineering for Tunable Synaptic Plasticity in Organic Electrochemical Neuromorphic Devices

Woojo Kim, Wen‐Ya Lee, Fabrizio Torricelli, Eun Kwang Lee, Hocheon Yoo, Sin‐Hyung Lee, Chang‐Jae Beak, Yonghee Kim
article en

Abstract

ABSTRACT Organic electrochemical transistors (OECTs) are widely studied for neuromorphic hardware, as their volumetric electrochemical doping couples ionic and electronic transport to mimic biological synaptic transmission. Research on OECT‐based artificial synapses has focused predominantly on the organic mixed ionic–electronic conductor channel, while the gate electrode has largely been treated as a passive input terminal. Yet because the gate electrode governs electrochemical injection, retention, and extraction of ions at the gate/electrolyte interface, it offers a direct and underexplored means of controlling synaptic plasticity. This review summarizes recent progress on gate electrode engineering for tuning plasticity in OECT‐based neuromorphic devices, organized around three interconnected axes: gate material chemistry, gate architecture and geometry, and gate dielectric/electrolyte interface engineering. We first outline OECT operating principles, the biological basis of synaptic plasticity, and standard neuromorphic figures of merit. We then examine how gate capacitance, ion selectivity, electrode geometry, and interfacial chemistry govern the transition from short‐ to long‐term plasticity across material classes spanning metals, carbons, conducting polymers, metal–organic frameworks, and emerging composites. Applications including associative learning, spike‐timing‐dependent plasticity, reservoir computing, and hardware neural networks are surveyed, and remaining challenges for scalable device integration are discussed.

Advanced Functional Materials
National Taipei University of Technology (TW), University of Seoul (KR), Gyeongsang National University (KR), Hanyang University (KR), University of Brescia (IT), Pukyong National University (KR)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
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