A Vertical Molecular Synaptic Transistor with Redox-Induced Analog States

We report a three-terminal, ion-gel-gated, redox-active molecular transistor that exhibits synaptic plasticity and analog conductance states. The device was composed of a ferrocene-terminated alkanethiolate self-assembled monolayer as the active channel, vertically sandwiched between a monolayer graphene source and a Au drain, and the channel conductance was modulated by an ion-gel gate. Gate voltage pulses induce an electric double layer at the ion-gel/graphene interface, triggering dynamic postsynaptic-like current responses. Our device exhibited neuroinspired plasticity, including short-term plasticity like paired-pulse facilitation and a programmable transition to long-term plasticity upon repeated stimulation. The ferrocene redox moiety was identified as the key enabler of nonvolatile switching behavior, mediating a dynamic, voltage-programmable conductance change via a synergistic mechanism of reversible redox and ion trapping. In contrast, alkanethiolate control devices without a ferrocene moiety exhibited only volatile, transient responses. We achieved multilevel conductance states with synaptic update characteristics depending on gate pulses, a crucial attribute for the learning process. As a proof of concept, a neural network simulated with our molecular synaptic transistor achieved ∼88% accuracy in MNIST pattern recognition, even after a single training epoch. These results establish vertical molecular transistor systems as promising building blocks for molecular-level neuromorphic hardware, with a three-terminal, read/write-decoupled architecture that exhibits synaptic behavior and helps overcome read-disturb of two-terminal memristive schemes.

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Publication Details

Journal
ACS Nano
Published
2025-12-19
DOI
https://doi.org/10.1021/acsnano.5c17016
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

A Vertical Molecular Synaptic Transistor with Redox-Induced Analog States

Takhee Lee, Changjun Lee, H.-S. Lee, Keehoon Kang et al.
ACS Nano
Advanced Memory and Neural Computing
article

A Vertical Molecular Synaptic Transistor with Redox-Induced Analog States

Takhee Lee, Changjun Lee, H.-S. Lee, Keehoon Kang, Jongwoo Nam, Gunuk Wang, D. Y. Kim, Minwoo Song
article en

Abstract

We report a three-terminal, ion-gel-gated, redox-active molecular transistor that exhibits synaptic plasticity and analog conductance states. The device was composed of a ferrocene-terminated alkanethiolate self-assembled monolayer as the active channel, vertically sandwiched between a monolayer graphene source and a Au drain, and the channel conductance was modulated by an ion-gel gate. Gate voltage pulses induce an electric double layer at the ion-gel/graphene interface, triggering dynamic postsynaptic-like current responses. Our device exhibited neuroinspired plasticity, including short-term plasticity like paired-pulse facilitation and a programmable transition to long-term plasticity upon repeated stimulation. The ferrocene redox moiety was identified as the key enabler of nonvolatile switching behavior, mediating a dynamic, voltage-programmable conductance change via a synergistic mechanism of reversible redox and ion trapping. In contrast, alkanethiolate control devices without a ferrocene moiety exhibited only volatile, transient responses. We achieved multilevel conductance states with synaptic update characteristics depending on gate pulses, a crucial attribute for the learning process. As a proof of concept, a neural network simulated with our molecular synaptic transistor achieved ∼88% accuracy in MNIST pattern recognition, even after a single training epoch. These results establish vertical molecular transistor systems as promising building blocks for molecular-level neuromorphic hardware, with a three-terminal, read/write-decoupled architecture that exhibits synaptic behavior and helps overcome read-disturb of two-terminal memristive schemes.

ACS NanoVol. 20(1)
Seoul National University (KR), Korea University (KR), PRG S&Tech (South Korea)
Openalex Percentile: Top 58%
Advanced Memory and Neural Computing
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