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Correlative Super-resolution and Electron Microscopy to Resolve Protein Localization in Zebrafish Retina

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Published on: November 10, 2017

Super-resolution method for SEM images based on pixelwise weighted loss function.

Akira Ito¹, Atsushi Miyamoto¹, Naoaki Kondo¹

¹Hitachi, Ltd., Research and Development Group, 292 Yoshida-cho, Totsuka-ku, Yokohama, Kanagawa 244-0817, Japan.

Microscopy (Oxford, England)

|February 4, 2023

Summary

Deep learning super-resolution enhances scanning electron microscopy (SEM) defect monitoring for semiconductor devices. This method doubles SEM throughput by generating high-resolution images from low-resolution inputs, improving defect detection.

Keywords:

SEM image to image translation loss function machine learning semiconductor inspection super-resolution

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Last Updated: Aug 11, 2025

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Published on: November 10, 2017

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Scanning electron microscopy (SEM) is crucial for high-throughput defect monitoring in semiconductor manufacturing.
Increasing semiconductor complexity necessitates higher SEM throughput for effective defect detection.

Purpose of the Study:

To develop a deep learning-based super-resolution method to enhance SEM imaging for defect monitoring.
To improve the throughput and image quality of SEM defect analysis.

Main Methods:

A deep learning super-resolution technique was employed to generate high-resolution (HR) SEM images from low-resolution (LR) inputs.
A pixelwise, pattern-adaptive loss calculation method was implemented to meet specific image quality requirements, including pattern contrast and sharpness.

Main Results:

The proposed method successfully generated super-resolved SEM images comparable to actual HR images.
The technique demonstrated the potential to improve SEM throughput by over 100%.

Conclusions:

Deep learning-based super-resolution offers a viable solution for increasing SEM throughput in semiconductor defect monitoring.
The method's adaptability in image quality control ensures its effectiveness for evaluating intricate circuit patterns.