Opto-Electrical Decoupling of Phototransistors via Light-Induced Ferroelectric Depolarization for In-Sensor Computing

Highly sensitive sensors are critical for in-sensor computing, an ultrafast and low-power machine vision technology. However, capturing sharp images without motion blur in low-light and high-speed situations remains challenging due to weak photoresponse. Here, we present a heterostructure ferroelectric phototransistor leveraging opto-electrical decoupling for fast perception and in-sensor computing. The channel is preprogrammed to a low-resistance state via ferroelectric polarization, while light modulates the drain current through light-induced ferroelectric depolarization. This mechanism enables a record-high MoTe<sub>2</sub>-based photoresponsivity of 3.05×10<sup>4</sup> A/W by optimizing the balance between depolarization and screening fields. The sensors can perceive light pulses as short as 200 μs, achieving an operating frequency of 5 kHz and an energy consumption of 74 fJ. Utilizing a light-programmable neutral point, a 3 × 3 sensor array was developed as the optical kernel for scene-specific in-sensor computing, achieving a license plate recognition accuracy of 92.4% with significantly reduced motion blur. These results demonstrate the potential of this technology for high-speed, low-light machine vision applications.

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Publication Details

Journal
ACS Nano
Published
2025-05-29
DOI
https://doi.org/10.1021/acsnano.5c04090
Citations
8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.84

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article

Opto-Electrical Decoupling of Phototransistors via Light-Induced Ferroelectric Depolarization for In-Sensor Computing

Xiulian Fan, Yu Zhou, Shengman Li, Chenguang Zhu et al.
8 citations
ACS Nano
Advanced Memory and Neural Computing
5.84
article

Opto-Electrical Decoupling of Phototransistors via Light-Induced Ferroelectric Depolarization for In-Sensor Computing

Xiulian Fan, Yu Zhou, Shengman Li, Chenguang Zhu, Jiali Yi, Guangcheng Wu, Tanghao Xie, Xingxia Sun, Anlian Pan, Yong Liu, Cheng Li, Dong Li, Shuai Qin, Huawei Liu, F. Yu
article en
8 citations

Abstract

Highly sensitive sensors are critical for in-sensor computing, an ultrafast and low-power machine vision technology. However, capturing sharp images without motion blur in low-light and high-speed situations remains challenging due to weak photoresponse. Here, we present a heterostructure ferroelectric phototransistor leveraging opto-electrical decoupling for fast perception and in-sensor computing. The channel is preprogrammed to a low-resistance state via ferroelectric polarization, while light modulates the drain current through light-induced ferroelectric depolarization. This mechanism enables a record-high MoTe<sub>2</sub>-based photoresponsivity of 3.05×10<sup>4</sup> A/W by optimizing the balance between depolarization and screening fields. The sensors can perceive light pulses as short as 200 μs, achieving an operating frequency of 5 kHz and an energy consumption of 74 fJ. Utilizing a light-programmable neutral point, a 3 × 3 sensor array was developed as the optical kernel for scene-specific in-sensor computing, achieving a license plate recognition accuracy of 92.4% with significantly reduced motion blur. These results demonstrate the potential of this technology for high-speed, low-light machine vision applications.

ACS NanoVol. 19(22)
Central South University (CN), Hunan University (CN), Hunan Normal University (CN), NeoPhotonics (United States) (US), Integrated Optoelectronics (Norway) (NO), Nanophoton (Brazil) (BR), State Key Laboratory of Chemobiosensing and Chemometrics (CN)
Key Project of Research and Development Plan of Hunan Province, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Wuhan National Laboratory for Optoelectronics, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.84
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