Authors
Shilin Lu, Ji Hoon Han, Dong Keun Lee, Sun Min Song, Huixin Yu, Sujin Jung, Lu Zhang, Zhao Yao, Jong Bin An, Hyun Jae Kim
Published in
Nano-micro letters. Volume 18. Issue 1. Jul 07, 2026. Epub Jul 07, 2026.
Abstract
Herein, a flexible dual-modal sensing transistor (FDST) is reported, based on zinc oxide nanofibers (ZnO NFs) integrated onto an indium-gallium-zinc-oxide thin-film transistor, and combined with a deep learning-based signal decoupling strategy. Defect-mediated subgap excitation and thermally activated interfacial potential modulation enable high sensitivity dual-modal responses, delivering a broadband photoresponsivity ( ) up to 2.69 A W-1 and a temperature coefficient ( ) of 0.071 °C-1. To enable reliable discrimination and simultaneous reconstruction of light and temperature, a multibias readout physically encodes the coupled stimuli into a high-dimensional current fingerprint, which is decoded by a lightweight multilayer perceptron. This synergistic approach enables accurate and independent reconstruction of light intensity and temperature, achieving coefficients of determination (R2) around 0.99. The FDST exhibits exceptional mechanical robustness under 10,000 bending cycles and severe bending (2 mm radius). Furthermore, a wearable system based on a low-power microcontroller demonstrates real-time monitoring with negligible cross-interference between optical and thermal modalities under uncontrolled outdoor conditions. This work establishes a general strategy for resolving cross-sensitivity, paving the way for robust and intelligent artificial perception systems.
PMID:
42412275
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 6
- Comments 0