Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network
Qilai Chen,
Tingting Han,
Jianmin Zeng,
Zhilong He,
Yulin Liu,
Jinglin Sun,
Minghua Tang,
Zhang Zhang,
Pingqi Gao,
Gang Liu
Affiliations
Qilai Chen
School of Materials, Sun Yat-Sen University, Guangzhou 510275, China
Tingting Han
Department of Micro and Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Jianmin Zeng
Department of Micro and Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Zhilong He
Department of Micro and Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yulin Liu
School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China
Jinglin Sun
Department of Micro and Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Minghua Tang
School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China
Zhang Zhang
School of Microelectronics, Hefei University of Technology, Hefei 230601, China
Pingqi Gao
School of Materials, Sun Yat-Sen University, Guangzhou 510275, China
Gang Liu
Department of Micro and Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs0.05FA0.81MA0.14 Pb(I0.85Br0.15)3 (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.