Deep-Learning-Aided Extraction of Optical Constants in Scanning Near-Field Optical Microscopy
arXiv:2202.02979 · doi:10.1063/5.0139517
Abstract
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. Compared to its traditional counterparts, our method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.
arXiv comment9 pages, 5 figures
References in corpus (18)
- Gate-tuning of graphene plasmons revealed by infrared nano-imaging
- Optical nano-imaging of gate-tuneable graphene plasmons
- Mott transition in VO2 revealed by infrared spectroscopy and nano-imaging
- Deep learning in nano-photonics: inverse design and beyond
- Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
- Microwave imaging of mesoscopic percolating network in a manganite thin film
- Bayesian optimization for tuning and selecting hybrid-density functionals
- Model for quantitative tip-enhanced spectroscopy and the extraction of nanoscale-resolved optical constants
- Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision
- Probes for Ultrasensitive THz Nanoscopy
- THz nanofocusing with cantilevered THz-resonant antenna tips
- Near-field spectroscopy of silicon dioxide thin films
- Tunneling Plasmonics in Bilayer Graphene
- Generalized spectral method for near-field optical microscopy
- Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy
- Scanning Probe State Recognition With Multi-Class Neural Network Ensembles
- Scattering of electromagnetic waves from a cone with conformal mapping: application to scanning near-field optical microscope
- Bayesian optimization for computationally extensive probability distributions