Issue

Vol. 18 (2026)

Flexible Dual-Modal Sensing Transistor Enabled by Deep Learning Decoupling for Independent Light and Temperature Reconstruction

https://doi.org/10.1007/s40820-026-02285-7
Shilin Lu
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Ji Hoon Han
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Dong Keun Lee
Department of Integrated Display Engineering, Yonsei University, Seoul 03722, Republic of Korea
Sun Min Song
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Huixin Yu
College of Electronic and Information, Qingdao University, Qingdao 266071, People’s Republic of China
Sujin Jung
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Lu Zhang
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Zhao Yao
College of Electronic and Information, Qingdao University, Qingdao 266071, People’s Republic of China
Jong Bin An
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Hyun Jae Kim
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea

Corresponding Author: Hyun Jae Kim

hjk3@yonsei.ac.kr

Nano-Micro Letters, Vol. 18 (2026), Article Number: 429

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.


Highlights:
1 A flexible dual-modal sensing transistor enabling simultaneous light and temperature detection was developed.
2 The device exhibits a broadband responsivity of 2.69 A W−1, a temperature coefficient of 0.071 °C−1, and stable operation under severe bending deformation.
3 A multibias electrical fingerprint combined with a lightweight neural network enables accurate reconstruction of light intensity and temperature for wearable environmental monitoring.

Keywords

Flexible electronics Light-temperature dual-modal sensing Thin-film transistors Deep learning Wearable systems
Lu, Shilin, Ji Hoon Han, Dong Keun Lee, Sun Min Song, Huixin Yu, Sujin Jung, Lu Zhang, Zhao Yao, Jong Bin An, and Hyun Jae Kim. 2026. “Flexible Dual-Modal Sensing Transistor Enabled by Deep Learning Decoupling for Independent Light and Temperature Reconstruction”. Nano-Micro Letters 18 (July):429. https://doi.org/10.1007/s40820-026-02285-7.
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