Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy
arXiv:2105.10551 · doi:10.1021/acsphotonics.1c00915
Abstract
The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.
References in corpus (13)
- Machine learning and the physical sciences
- Gate-tuning of graphene plasmons revealed by infrared nano-imaging
- A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
- Physics-informed neural networks for inverse problems in nano-optics and metamaterials
- Deep-STORM: super-resolution single-molecule microscopy by deep learning
- Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
- Model for quantitative tip-enhanced spectroscopy and the extraction of nanoscale-resolved optical constants
- THz nanofocusing with cantilevered THz-resonant antenna tips
- Multi-messenger nano-probes of hidden magnetism in a strained manganite
- Generalized spectral method for near-field optical microscopy
- Moiré Engineering of Electronic Phenomena in Correlated Oxides
- Quantifying nanoscale electromagnetic fields in near-field microscopy by Fourier demodulation analysis
- Substrate matters: Surface-polariton enhanced infrared nanospectroscopy of molecular vibrations
Cited by in corpus (8)
- Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
- Deep learning analysis of polaritonic waves images
- Deep-Learning-Aided Extraction of Optical Constants in Scanning Near-Field Optical Microscopy
- Nanoscale local modification of PMMA refractive index by tip-enhanced femtosecond pulsed laser irradiation
- Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
- Quantitative Analytical Model for Scattering-type Scanning Near-field Optical Spectroscopy
- Simulation of Scanning Near-Field Optical Microscopy Spectra of 1D Plasmonic Graphene Junctions
- Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory