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

作者：Guangcheng Wu, F. Yu, Jiali Yi, Huawei Liu, Xiulian Fan, Cheng Li, Chenguang Zhu, Xingxia Sun, Yong Liu, Shuai Qin, Tanghao Xie, Shengman Li, Yu Zhou, Dong Li, Anlian Pan · 发表于：ACS Nano · 年份：2025 · DOI：10.1021/acsnano.5c04090 · 被引用次数：12 · 研究领域：Advanced Memory and Neural Computing、Analytical Chemistry and Sensors、Mechanical and Optical Resonators

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 2 -based photoresponsivity of 3.05×10 4 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.