Revealing ferroelectric switching character using deep recurrent neural networks.

ABSTRACT: The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr_0.2Ti_0.8O₃ with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic

SUBMITTER: Agar JC 

PROVIDER: S-EPMC6805893 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature