journal article·2025-07-22

Fluorinated Self-Assembled Monolayer Ion Receptors for Retentive Analog Synaptic Behavior

Minho Jin, Jae Hak Lee, Haeyeon Lee, Jiho Lee, Jong Chan Shin, Chan Lee, Eungkyu Lee, Youn Sang Kim

ACS Nano· Volume 19· Issue 28
Publisher page

Abstract

Numerous studies have highlighted ionic transistors as a promising candidate for retentive artificial synapses in analog neuromorphic computing, owing to their exceptional characteristics of analog signal modulation and low energy consumption. However, the self-discharge of ions at the channel-electrolyte interface limits their retention characteristics. Therefore, it is crucial to control the ion discharge kinetics by engineering interfacial electrostatic interactions. By precisely tuning the ion discharging time through interface design, ionic transistors can achieve the stable, long-lasting charge retention required for retentive artificial synapses in training acceleration and inference. Herein, a principled approach is proposed to enhance ion interactions by introducing a fluorinated self-assembled monolayer (F-SAM), Heneicosafluorododecyl phosphonic acid (F 21 -DDPA), as an ion receptor in electrolyte-gated transistors (EGTs). This device induces strong ion-dipole interactions between lithium ions and negatively charged F 21 -DDPA at the channel/electrolyte interface. Incorporating F 21 -DDPA allows the lithium ions to be trapped at the interface, enabling the EGTs to maintain near-linearly tunable multimodal conductance states. Additionally, through chemical analysis and first-principles density functional theory calculations, F 21 -DDPA can facilitate sequential lithium ion trapping at the interface by ion-dipole forces. This approach provides a fundamental solution to understanding ion dynamics at the interface and resolving the issues associated with ionic transistors for applications in artificial synapses.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Research Institute for Convergence Science, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Samsung Display Co, Ltd.

Department of Chemical and Biological Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Department of Electronic Engineering

Kyung Hee University

Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Advanced Institute of Convergence Technology, 145 Gwanggyo-ro, Yeongtong-gu, Suwon 16229, Republic of Korea

Funding

National Research Foundation of Korea

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