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Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis

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Sparsity-Based Super Resolution for SEM Images.

Shahar Tsiper, Or Dicker, Idan Kaizerman¹

¹Applied Materials , 9 Oppenheimer St., Rehovot 76705, Israel.

|August 15, 2017

Summary

This study introduces a new method using sparse coding to improve scanning electron microscope (SEM) images of microelectronic chips. The technique enhances low-resolution images to high-resolution quality, reducing noise and increasing scanning speed.

Keywords:

Super resolution dictionary learning microscopy scanning electron microscope (SEM)sparsity

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Area of Science:

Materials Science
Nanotechnology
Computer Science

Background:

Scanning Electron Microscopes (SEM) are crucial for nanometer-scale imaging in research and industry.
Achieving high resolution (HR) in SEM requires extensive scanning, limiting throughput for large areas.
Current SEM diagnostics often involve a trade-off between resolution and area coverage for microelectronic chips.

Purpose of the Study:

To develop an algorithmic method for enhancing low-resolution (LR) SEM images of microelectronic chips.
To achieve high-resolution image quality from LR scans without compromising analysis quality.
To significantly increase SEM scanning throughput for microelectronics analysis.

Main Methods:

Employed sparse coding and dictionary learning techniques.
Developed a two-stage methodology: offline dictionary learning from LR/HR image pairs, followed by fast online super-resolution.
Applied the method to SEM images of microelectronic chips.

Main Results:

Successfully enhanced LR SEM images to the level of HR images.
Demonstrated considerable noise reduction in the enhanced images.
Significantly increased scanning throughput by enabling HR analysis on larger chip areas.

Conclusions:

Sparse coding and dictionary learning offer a powerful approach to improve SEM performance.
The proposed method enhances SEM image quality and analysis speed for microelectronics.
This technique addresses the resolution-area trade-off in SEM diagnostics.