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 PbZr0.2Ti0.8O3 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 hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material’s physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.
Highlights
The ability to manipulate domains underpins function in applications of ferroelectrics
Developments in multimodal spectroscopy allow the acquisition of data at both the appropriate time- and length scales required to glean such information from ferroelectric materials using techniques such as transmission electron microscopy[21,22], scanning-probe microscopy[23,24], diffraction studies[25,26], etc.[27]
The resulting films have a hierarchical domain structure with a sawtooth topography on two length scales (Fig. 1a–e), as the result of primarily out-of-plane polarized c/a/c/a [with enhanced out-of-plane (Fig. 1b) and suppressed in-plane (Fig. 1c) piezoresponse] and fully in-plane polarized a1/a2/a1/a2 [with suppressed out-of-plane and enhanced in-plane piezoresponse] domain bands. This hierarchical domain structure emerges due to the tensile strain which drives the c/a and a1/a2 domain variants to be nearly energetically degenerate (Supplementary Note 2)[6]
Summary
The ability to manipulate domains underpins function in applications of ferroelectrics. We develop and train a deep-learning-neural-network-based sparse autoencoder on piezoresponse hysteresis loops to demonstrate parity with conventional empirical-analysis approaches We apply this approach to extract insight from the resonance response which has a form too complex to be properly analyzed using techniques common in experimental materials science. Using the information “learned”, we identify geometrically driven differences in the switching mechanism which are related to charged-domain-wall nucleation and growth during ferroelastic switching This insight could not have been extracted using machine-learning approaches that have been previously applied to materials spectroscopy and provides unprecedented information about the nature of the specific domain-structure geometries that should be explored to enhance local and macroscale susceptibilities. This work represents an example of how unsupervised deep learning can highlight features relating to ferroelectric physics overlooked by human-designed-machinelearning algorithms, and how such approaches can be adapted to analyze hyperspectral data more broadly
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