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Joshua Agar2 1 Brett Naul1 Shishir Pandya1 Stefan van der Walt1 Joshua Maher1 Ren Yao3 Tess Schmidt1 Jeffrey Neaton1 Sergei Kalinin4 Rama Vasudevan4 Ye Cao3 Joshua Bloom1 Lane Martin1

2, Materials Science and Engineering, Lehigh University, Bethlehem , Pennsylvania, United States
1, University of California, Berkeley, Berkeley, California, United States
3, Materials Science and Engineering, The University of Texas at Arlington, Arlington, Texas, United States
4, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

The ability to create and manipulate domain structures in ferroelectrics allows the control of the phase and polarization orientation, imparting deterministic changes to the local and macroscale susceptibilities (e.g., electrical, thermal, mechanical, optical, etc.) providing a foundation for next-generation devices. While there have been demonstrations of nanoscale manipulation and control of such structures the majority of this work, however, has focused on the static creation of desired domain structures, and thus lacks an internal self-regulating feedback loop required for automatic operation in functional devices. Here, we develop an unsupervised sequence-to-sequence neural network, which considers the temporal dependence in the data, to extract inference from band-excitation piezoresponse spectroscopy (BEPS). To test our approach, we conducted BEPS on a tensile-strained PbZr0.2Ti0.8O3thin films wherein strain drives the formation of a hierarchical c/aand a1/a2domain structure. We develop and train a deep-learning-neural-network-based sparse autoencoder on the piezoresponse hysteresis loops to demonstrate parity with conventional approaches. We then apply this approach to extract insight from the resonance response which has a form too complex to be properly analyzed using conventional techniques. 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 conventional machine-learning approaches and provides unprecedented information about the nature of the specific domain-structure geometries that should be explored to enhance local and macroscale susceptibilities. Furthermore, the ability to automate the extraction of inference regarding ferroelectric switching from multichannel nanoscale spectroscopy provides a route for real-time controlled creation and verification of interconversion of functional domain structures and interfaces. The developed approach is extensible to other forms of multi-dimensional, hyper-spectral (wherein there is a spectra at each pixel) images which are commonly acquired in: time-of-flight secondary-ion mass spectrometry, scanning Raman, electron energy loss spectroscopy, etc.To promote the utilization of this approach, we provide open access to all data and codes in the form of a Jupyter notebook. Ultimately, this work represents an example of how unsupervised deep learning can highlight features relating to ferroelectric physics overlooked by human-designed-machine-learning algorithms, and how such approaches can be broadly adapted to analyze hyperspectral data.

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