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Vol. 18 (2026)

Ferroelectric Optoelectronic Sensor for Intelligent Flame Detection and In-Sensor Motion Perception

https://doi.org/10.1007/s40820-025-01968-x
Jiayun Wei
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Guokun Ma
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Runzhi Liang
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Wenxiao Wang
School of Physics, South China Normal University, Guangzhou 510006, People’s Republic of China
Jiewei Chen
School of Physics, South China Normal University, Guangzhou 510006, People’s Republic of China
Shuang Guan
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Jiaxing Jiang
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Ximo Zhu
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Qian Cheng
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Yang Shen
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Qinghai Xia
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Shiwen Wu
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Houzhao Wan
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Longhui Zeng
School of Physics and Microelectronics, Key Laboratory of Material Physics Ministry of Education, Zhengzhou University, Zhengzhou 450052, People’s Republic of China
Mengjiao Li
School of Microelectronics, Shanghai University, Jiading, Shanghai 201800, People’s Republic of China
Yi Wang
Center for Microscopy and Analysis, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People’s Republic of China
Liangping Shen
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Wei Han
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China
Hao Wang
School of Integrated Circuits, Hubei University, Wuhan 430062, People’s Republic of China

Corresponding Author: Hao Wang

wangh@hubu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 123

Abstract

Next-generation fire safety systems demand precise detection and motion recognition of flames. In-sensor computing, which integrates sensing, memory, and processing capabilities, has emerged as a key technology in flame detection. However, the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet (UV) band (200–280 nm) is hindered by the weak detection capability. Here, we propose Ga2O3/In2Se3 heterojunctions for the ferroelectric (abbreviation: Fe) optoelectronic sensor (abbreviation: OES) array (5 × 5 pixels), which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality. The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system. Moreover, the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware. Using this Fe-OES system and neuromorphic hardware, we successfully demonstrate three flame processing tasks: achieving efficient flame detection across all time periods with terminal and cloud-based alarms; flame motion recognition with a lightweight convolutional neural network achieving 96.47% accuracy; and flame light recognition with 90.51% accuracy by means of a photosensitive artificial neural system. This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.


Highlights:
1 The Ga₂O₃/In₂Se₃ heterojunction ferroelectric optoelectronic sensor array enables precise detection of ultraweak UV signals through ferroelectric modulation.
2 Efficient flame detection across all time periods is achieved through terminal devices and cloud-based alert systems.
3 The lightweight convolutional neural network-based approach achieves a flame motion recognition accuracy of 96.47%, while the optoelectronic artificial neural system attains 90.51% accuracy in identifying flame optical signals.

Keywords

Gallium oxide Indium selenide Flame detection Flame motion recognition
Wei, Jiayun, Guokun Ma, Runzhi Liang, Wenxiao Wang, Jiewei Chen, Shuang Guan, Jiaxing Jiang, Ximo Zhu, Qian Cheng, Yang Shen, Qinghai Xia, Shiwen Wu, Houzhao Wan, Longhui Zeng, Mengjiao Li, Yi Wang, Liangping Shen, Wei Han, and Hao Wang. 2026. “Ferroelectric Optoelectronic Sensor for Intelligent Flame Detection and In-Sensor Motion Perception”. Nano-Micro Letters 18 (January):123. https://doi.org/10.1007/s40820-025-01968-x.
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