Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network

作者：Qi Chen, Tingting Han, Jianmin Zeng, Zhilong He, Yulin Liu, Jinglin Sun, Minghua Tang, Zhang Zhang, Pingqi Gao, Gang Liu · 发表于：Nanomaterials · 年份：2022 · DOI：10.3390/nano12132217 · 被引用次数：15 · 研究领域：Perovskite Materials and Applications、Advanced Memory and Neural Computing、Photoreceptor and optogenetics research

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.