Open AccessSwitchable Perovskite Photovoltaic Sensors for Bioinspired Adaptive Machine Vision
Author(s) -
Chen Qilai,
Zhang Ying,
Liu Shuzhi,
Han Tingting,
Chen Xinhui,
Xu Yanqing,
Meng Ziqi,
Zhang Guanglei,
Zheng Xuejun,
Zhao Jinjin,
Cao Guozhong,
Liu Gang
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000122
Subject(s) - computer science , artificial intelligence , photovoltaic system , computer vision , machine vision , perovskite (structure) , electrical engineering , engineering , chemical engineering
Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.