Monolithic 3D‐Integrated All‐Solid Ion‐Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi‐Timescale Reservoir Computing

Ion-gated transistors inherently exhibit time-dependent behavior governed by ionic motion associated with electric double-layer formation; however, their practical implementation has been limited by insufficient control over ionic dynamics and poor compatibility with scalable thin-film integration. Here, we present carbon nanotube (CNT) solid-ion-gated transistors (sIGTs) that allow the wide-range engineering of ionic dynamics while remaining fully compatible with wafer-scale thin-film processing. Tunable ionic conductance is achieved by ionic content engineering in the film and thickness scaling into the sub-micron regime, enabling ionic time constants from microseconds to milliseconds. CNT sIGTs demonstrate robust DC operation at low ionic content with an optimized polymer matrix and wafer-scale fabrication on flexible substrates. Frequency-dependent gate modulation governed by ionic conductance is systematically investigated through electrical impedance spectroscopy and small-signal analysis, including a comparison of the -3 dB cutoff frequency and the transit frequency. This analysis provides direct insight into the relationship between ionic conductance and frequency-dependent device response, exhibiting consistent trends across both two-terminal and three-terminal device configurations. Monolithic three-dimensional integration of two-tier CNT sIGTs with engineered dynamic responses is demonstrated as a compact dual-timescale physical reservoir for neuromorphic computing that enables classification of time-varying inputs using a single readout layer.

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Publication Details

Journal
Advanced Materials
Published
2026-06-30
DOI
https://doi.org/10.1002/adma.202523703
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Monolithic 3D‐Integrated All‐Solid Ion‐Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi‐Timescale Reservoir Computing

Joonki Suh, Hanbin Cho, Jimin Kwon, Yun Goo Ro et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Monolithic 3D‐Integrated All‐Solid Ion‐Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi‐Timescale Reservoir Computing

Joonki Suh, Hanbin Cho, Jimin Kwon, Yun Goo Ro, Hyunhyub Ko, Yongwoo Lee, Haksoon Jung, Seunghun Baek, Hyeongjun Kim, Yong‐Young Noh
article en

Abstract

Ion-gated transistors inherently exhibit time-dependent behavior governed by ionic motion associated with electric double-layer formation; however, their practical implementation has been limited by insufficient control over ionic dynamics and poor compatibility with scalable thin-film integration. Here, we present carbon nanotube (CNT) solid-ion-gated transistors (sIGTs) that allow the wide-range engineering of ionic dynamics while remaining fully compatible with wafer-scale thin-film processing. Tunable ionic conductance is achieved by ionic content engineering in the film and thickness scaling into the sub-micron regime, enabling ionic time constants from microseconds to milliseconds. CNT sIGTs demonstrate robust DC operation at low ionic content with an optimized polymer matrix and wafer-scale fabrication on flexible substrates. Frequency-dependent gate modulation governed by ionic conductance is systematically investigated through electrical impedance spectroscopy and small-signal analysis, including a comparison of the -3 dB cutoff frequency and the transit frequency. This analysis provides direct insight into the relationship between ionic conductance and frequency-dependent device response, exhibiting consistent trends across both two-terminal and three-terminal device configurations. Monolithic three-dimensional integration of two-tier CNT sIGTs with engineered dynamic responses is demonstrated as a compact dual-timescale physical reservoir for neuromorphic computing that enables classification of time-varying inputs using a single readout layer.

Advanced Materials
Pohang University of Science and Technology (KR), Korea Advanced Institute of Science and Technology (KR), Ulsan National Institute of Science and Technology (KR)
National Research Foundation of Korea
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
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