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.
Authors
- Joonki Suh (ORCID: https://orcid.org/0000-0002-0221-8447)
- Hanbin Cho
- Jimin Kwon (ORCID: https://orcid.org/0000-0002-5213-1323)
- Yun Goo Ro (ORCID: https://orcid.org/0000-0002-7023-086X)
- Hyunhyub Ko (ORCID: https://orcid.org/0000-0003-2111-6101)
- Yongwoo Lee (ORCID: https://orcid.org/0000-0002-9367-0235)
- Haksoon Jung (ORCID: https://orcid.org/0000-0003-1567-692X)
- Seunghun Baek
- Hyeongjun Kim
- Yong‐Young Noh
Institutions
- Pohang University of Science and Technology (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Ulsan National Institute of Science and Technology (KR)
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
Funders
- National Research Foundation of Korea