Probing Local Structure and Polarisation Under Static and Dynamic Conditions of Lead-Free Ferroelectrics Using 4D STEM
Student Contest:
No
Affiliation Type:
Academia
Keywords:
lead-free ferroelectrics, BFO, KNN, 4DSTEM, AI
Abstract:
4D STEM reveals ferroelectric domain structures and their evolution under stimuli, providing insights into crystallographic orientation and stress fields. This technique, using datasets from ARM 200 CF and Spectra 300 microscopes, coupled with simulated data (QSTEM, DFT calculations), allows the investigation of point defects (e.g., cation/oxygen vacancies) in materials like BFO and KNN. AI analysis of these datasets helps understand how defects influence polarization, dynamics, and domain behaviour, enabling targeted manipulation and optimization of ferroelectric properties for improved device performance. In situ biasing experiments further enhance the understanding of domain growth, coalescence, and wall interactions.
Track ID:
1.4
Track Name:
ISAF: Characterization & Properties of Ferroelectrics & Related Materials
