AI Article Synopsis
- Deep learning (DL) is increasingly used in scientific and engineering fields, particularly for analyzing large imaging data sets.
- The researchers applied a convolutional neural network (CNN) to analyze nanoscale images of polaritonic waves, creating a method to quickly quantify wavelengths and quality factors.
- Their CNN-based protocol significantly speeds up data processing—at least 1,000 times faster than traditional methods—and has been validated using experimental images from specific material interfaces.
Article Abstract
Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acsnano.1c07011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!