Fluorinated Self-Assembled Monolayer Ion Receptors for Retentive Analog Synaptic Behavior
2025 · ACS Nano
저자
Minho Jin, Jae‐Hak Lee, Haeyeon Lee, Jiho Lee, Jong Chan Shin, Chan Young Lee, Eungkyu Lee, 김연상 (Youn Sang Kim)
연구주제
Advanced Memory and Neural Computing · Conducting polymers and applications
NTIS 과학기술표준분류
가장 가까운 분류(물리학 > 응집물질물리 > 나노구조/나노소자)가 화학·화공 범위 밖이다. 배정 근거: AI 키워드가 잡은 NTIS 과제의 분류.
AI 연구 키워드·방법
인공 시냅스 전해질 게이트 트랜지스터 불소화 자기조립 단분자막(F-SAM)
키워드 (OpenAlex)
fluorinated self-assembled monolayers electrolyte-gated transistors artificial synapses charge retention ion-dipole interactions neuromorphic computing conductance modulation
초록
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 rec…
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교수 1
연구주제 1
Advanced Memory and Neural Computing