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Updated: Jun 12, 2026

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
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Published on: May 30, 2016

Deep-learning-assisted scattering structured-illumination confocal microscopy for industrial super-resolution

Peng Du, Meiting Wang, Xinran Li

    Optics Express
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We developed a deep learning-based scattering laser scanning structural illumination microscope (DL-sLSSIM) to overcome limitations in industrial inspection. This advanced microscopy technique achieves super-resolution imaging for semiconductor nanostructures, enhancing inspection fidelity.

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    Published on: September 29, 2014

    Area of Science:

    • Optics and Photonics
    • Materials Science
    • Artificial Intelligence

    Background:

    • Laser scanning confocal microscopy (LSCM) is crucial for industrial inspection but faces limitations like the edge shadow effect and Abbe diffraction limit.
    • These limitations hinder precise nanostructure inspection in semiconductor manufacturing.
    • Existing methods struggle with distorted structured light patterns in complex industrial samples.

    Purpose of the Study:

    • To propose a novel deep learning-driven scattering laser scanning structural illumination microscope (DL-sLSSIM).
    • To achieve super-resolution inspection of semiconductor nanostructures.
    • To overcome the limitations of conventional microscopy in industrial metrology.

    Main Methods:

    • Developed a polarization-compensated scattering confocal configuration to enhance edge contrast and suppress background.
    • Analyzed the imaging mechanism of point-scanning structured illumination and identified stripe distortion issues.
    • Introduced a residual channel attention network (RCAN) for nonlinear demodulation and high-frequency information extraction.

    Main Results:

    • Achieved a lateral resolution improvement of approximately 1.8×, reaching 480 nm under low-magnification inspection.
    • Significantly improved the signal-to-noise ratio in imaging semiconductor wafers.
    • Demonstrated the effectiveness of DL-sLSSIM for super-resolution metrology in advanced manufacturing.

    Conclusions:

    • DL-sLSSIM provides a high-fidelity, non-destructive super-resolution metrology solution for semiconductor manufacturing.
    • The integration of deep learning with scattering structured illumination overcomes conventional imaging barriers.
    • This approach enables enhanced inspection of nanostructures with improved resolution and clarity.