Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

نویسندگان

چکیده

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where interaction between and sample nontrivial. Realistic modeling to include details of always exponentially more difficult than its "spherical cow" counterparts. On other hand, well-trained artificial neural network based on real data can grasp hidden correlation signal properties. In this work, we show that, via combination model calculation acquisition, physics-infused hybrid predict tip-sample in widely used scattering-type near-field optical microscope. provides long-sought solution accurate extraction material properties from tip-specific raw data. methodology be extended techniques as well data-oriented physical problems general.

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ژورنال

عنوان ژورنال: ACS Photonics

سال: 2021

ISSN: ['2330-4022']

DOI: https://doi.org/10.1021/acsphotonics.1c00915