Delineating complex ferroelectric domain structures via second harmonic generation spectral imaging  

作　　者：Wei Li Yunpeng Ma Tianyi Feng Ziwan Du Yixuan Liu Sergei VKalinin Jing-Feng Li Qian Li 

机构地区：[1]State Key Laboratory of New Ceramics and Fine Processing,School of Materials Science and Engineering,Tsinghua University,Beijing,100084,China [2]Department of Materials Science and Engineering,University of Tennessee Knoxville,Knoxville,TN,37996,USA

出　　处：《Journal of Materiomics》2023年第2期395-402,共8页无机材料学学报（英文）

基　　金：supported by National Natural Science Foundation of China(NSFC)under Grants No.52073155 and No.52150092;the National Key Basic Research Program of China under Grant No.2020YFA0309100,and by the Project for Introduced Innovation Team in Jinan(2020GXRC037).

摘　　要：Understanding the mechanisms and spatial correlations of crystallographic symmetry breaking in ferroelectric materials is essential to tuning their functional properties.While optical second harmonic generation(SHG)has long been utilized in ferroelectric studies,its capability for probing complex polar materials has yet to be fully realized.Here,we develop a SHG spectral imaging method implemented on a home-designed laser-scanning SHG microscope,and demonstrate its application for a model system of(K,Na)NbO3 single crystals.Supervised model fitting analysis produces comprehensive information about the polarization vector orientations and relative fractions of constituent domain variants as well as their thermal evolution across the polymorphic phase transitions.Multiple domains and phases are clearly delineated at different temperatures,suggesting the phase competitions in(K,Na)NbO3.Besides,we show that unsupervised matrix decomposition analysis can quickly and faithfully reveal domain configurations without a priori knowledge about specific material systems.The SHG spectral imaging method can be readily extended to other ferroelectric materials with potentials to be further enhanced.

关 键 词：Second harmonic generation FERROELECTRICS Domain structure Bayes linear unmixing 

分 类 号：O657.3[理学—分析化学]