@article{Sun2025, 
author = {Jie Sun and Xinyuan Wang and Yuchen Cai and Xiyan Pan and Ke Jin and Xiangyu Chen and Keyou Yan and Xuhui Zhu and Tao Jiang and Junliang Yang and Shaomin Ji and Yongbo Yuan and Jingjing Chang and Liming Ding},
title = {Electrically/optically modulated polarization photodetector made of ultrafine perovskite nanoripples},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {6},
pages = {94907493},
keywords = {perovskite, polarization-sensitive, ion migration, electro-optic effect, CH3NH3PbCl3},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907493},
doi = {10.26599/NR.2025.94907493},
abstract = {Intensity, wavelength, and polarization are fundamental properties of light and typically represent the three detection dimensions in photodetectors. The information related to polarization includes additional subdimensions such as polarization state and phase difference, which can be resolved using crystals with birefringence. Inorganic materials exhibiting the electro-optic effect, like lithium niobate, are commonly employed in electro-optic modulation optical systems. However, their limited functionality poses challenges for integrating optical systems. Halide perovskites have been found to exhibit electro-optic effects due to symmetry loss in their crystal structures, making them promising candidates. These perovskites also serve as active materials with outstanding optoelectronic properties, providing opportunities for integrating electro-optically modulated polarization detection systems. This paper proposes a novel photodetector that utilizes electro-optic modulation of polarization response. The ultrafine nanoripples on the surface of the perovskite, along with micron-sized perovskite crystals, facilitate both polarization response and electro-optic modulation. This combination, enhanced by the exceptional optoelectronic characteristics of perovskites, enables the visualization of polarization modulation and the generation of multidimensional polarization currents, which can be leveraged for optical system integration and applications in machine learning.}
}