Microscale image encoding on metal halide perovskite thin films for anti-counterfeiting applications

Abstract

Metal halide perovskite thin films are a demand in many technological areas since they fulfill the requirements expected in different applications, including solar cells and LEDs. In this article, we demonstrate for the first time that perovskite thin films have the potential to be incorporated in products as an anticounterfeit solution in self-identification methods due to their ability to generate images on its surface with motifs at the microscale range. The images can be recorded using an optical microscope with a camera and can be recognized automatically using a convolutional neural network. The surface pattern of the thin films can be designed by modifying the perovskite composition, structure, and surface strain making falsification difficult to reproduce and mimic. To this end, perovskite thin films have been characterized using X-ray diffraction, UV-Vis spectroscopy and optical microscopy. We report a batch of 5 types of Cs_xFA_1−xPbI_3−xBr_x perovskite thin films obtained by modifying the experimental conditions that exhibit recognized patterns with accuracy detection of 94.2%.