Tactile Neuromorphic Ion-Gated Vertical Transistor Displays Enabling Dual-Output Reservoir Computing
Integrating tactile sensing, neuromorphic processing, and visual emission within a single device is essential for next-generation intelligent interfaces. Although tactile neuromorphic displays have advanced significantly, further advances in brightness, stable continuous emission, and gate-tunable optical modulation would substantially broaden their applicability. Here, we present a tactile neuromorphic ion-gated vertical transistor display (TN-VTD) that integrates pressure sensing, ion-mediated synaptic plasticity, and electroluminescence within a compact vertical structure. Mechanical deformation of a hemispherical elastomeric gate modulates ion penetration through a permeable nanoporous Al electrode, inducing pressure-dependent n-type doping and band bending within the Super Yellow emissive layer. This ion-coupled design enables continuous and stable DC emission (∼46 cd m –2 at V DS = 3 V) for over 24 h and strong gate-enhanced brightness (∼427 cd m –2 at V G = 9 V), with the emissive area expanding proportionally with applied pressure. Pressure-dependent ionic accumulation further tunes synaptic relaxation time, generating nonlinear fading-memory electrical and optical responses that evolve along distinct temporal dynamics. From a single input, concurrent conductance and brightness outputs provide complementary state variables that expand the reservoir state dimensionality beyond single-observable operations. Leveraging this multidimensional state encoding, TN-VTD achieves 93.5% spoken-digit classification accuracy, substantially outperforming conductance-only operation (82.3%). These results establish TN-VTD as a unified platform capable of simultaneous sensing, neuromorphic processing, and visual output.
Authors
- HoYeon Kim (ORCID: https://orcid.org/0009-0002-0931-1019)
- Woojoong Kim (ORCID: https://orcid.org/0000-0003-3980-0614)
- 하제봉
- G Y Kim
- KB Huh
- Seong-Jin Park (ORCID: https://orcid.org/0000-0002-8186-0824)
- Seung Jun Lee (ORCID: https://orcid.org/0000-0002-1849-5086)
- Jungjae Park (ORCID: https://orcid.org/0000-0003-4637-6727)
- Cheolmin Park (ORCID: https://orcid.org/0000-0002-6832-0284)
- J J Jang
- Jioh Yoo
- Junhyeok Song
- Shengyou Li
- Ning Li
- Changhoe Lee
Institutions
- Yonsei University (KR)
- Convergence (US)
- Korea Institute of Science and Technology (KR)
Publication Details
- Journal
- ACS Nano
- Published
- 2026-06-30
- DOI
- https://doi.org/10.1021/acsnano.6c05264
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00