Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Broadband coherent Fourier scatterometry: A two-pulse approach.

The Review of scientific instruments·2025
Same author

Transversal optical singularity induced precision measurement of step-nanostructures.

Optics express·2023
Same author

Optical singularity assisted method for accurate parameter detection of step-shaped nanostructure in coherent Fourier scatterometry.

Optics express·2022
Same author

Direct detection of polystyrene equivalent nanoparticles with diameter of 21 nm (∼λ/19) using coherent Fourier scatterometry: erratum.

Optics express·2022
Same author

Direct detection of polystyrene equivalent nanoparticles with a diameter of 21 nm (∼λ/19) using coherent Fourier scatterometry.

Optics express·2021
Same author

Superresolution effect due to a thin dielectric slab for imaging with radially polarized light.

Optics express·2020

Related Experiment Video

Updated: Dec 7, 2025

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

12.8K

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data.

D Kolenov, D Davidse, J Le Cam

    Applied Optics
    |September 25, 2020
    PubMed
    Summary

    Deep learning accurately classifies nanoparticles on surfaces using scatterometry data. This convolutional neural network method significantly improves upon existing techniques for nanoparticle detection and classification.

    More Related Videos

    Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
    09:30

    Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

    Published on: May 24, 2019

    7.7K
    Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging
    07:41

    Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging

    Published on: July 19, 2016

    8.0K

    Related Experiment Videos

    Last Updated: Dec 7, 2025

    Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
    12:19

    Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

    Published on: December 8, 2015

    12.8K
    Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
    09:30

    Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

    Published on: May 24, 2019

    7.7K
    Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging
    07:41

    Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging

    Published on: July 19, 2016

    8.0K

    Area of Science:

    • Optics
    • Materials Science
    • Computer Science

    Background:

    • Scatterometry is crucial for nanoparticle analysis but is often slow and manual.
    • Deep learning offers automated feature extraction and classification, applicable to optical data.

    Purpose of the Study:

    • To develop and validate a deep learning model for efficient nanoparticle detection and classification from 2D scatterometry maps.
    • To compare the deep learning approach against traditional methods for performance enhancement.

    Main Methods:

    • Collected experimental scatterometry data for four classes of polystyrene nanoparticles (40-80 nm) and a background.
    • Trained and optimized a convolutional neural network (CNN) architecture.
    • Implemented a supervisor layer for enhanced rejection of non-target images.

    Main Results:

    • Achieved 95% accuracy in nanoparticle classification.
    • Demonstrated up to twofold performance enhancement compared to line-by-line search and thresholding.
    • The supervisor layer rejected up to 80% of fooling images while only rejecting 10% of original data.

    Conclusions:

    • Deep learning, specifically CNNs, provides a highly accurate and efficient solution for nanoparticle classification in scatterometry.
    • The developed method significantly outperforms traditional techniques, offering faster and more reliable analysis.
    • The open-source code and dataset facilitate further research and application in nanoparticle characterization.