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arXiv · 2104.10207

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

Yongtao Liu·Rama K. Vasudevan·Kyle Kelley·Dohyung Kim·Yogesh Sharma·Mahshid Ahmadi·Sergei V. Kalinin·Maxim Ziatdinov
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Abstract

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy (BE-PFM) data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of X-ray diffraction, photoluminescence, Raman spectra, and other data sets.

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Yongtao Liu, Rama K. Vasudevan, Kyle Kelley, Dohyung Kim, Yogesh Sharma, Mahshid Ahmadi, Sergei V. Kalinin, Maxim Ziatdinov. 2021-04-20. Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy. https://arxiv.org/abs/2104.10207

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