Single Image Signal-to-Noise Ratio (SNR) Estimation Techniques for Scanning Electron Microscope: A Review

Abstract

Noises are commonly present in grayscale images, particularly in Scanning Electron Microscope (SEM) images especially secondary emission noise, which can significantly affect their quality. Signal-to-Noise Ratio (SNR) estimation techniques are crucial for assessing the quality of these images by quantifying the ratio of signal strength to background noise. Traditionally, SNR estimation required two microscope images of identical sample areas, which made it difficult to determine the SNR of existing micrographs or stored images and resulted in a time-consuming process due to the necessity for precise alignment. To address these challenges, innovators have developed methods to assess an image’s SNR value using only a single image, thereby eliminating the alignment problem and speeding up the process. The single image SNR estimation can be used in various fields such as semiconductor and biological structure analysis. Additionally, SNR estimation methods can be used to accurately estimate SNR values, remove noise from images, identify IC cracks, avoid sample contamination, and reduce the charging effect. This review paper explains and analyzes the working principles of SEM, the various types of noise present in SEM images, and the SNR estimation techniques used to mitigate these issues. We cover both traditional two-image methods and modern single-image methods, discussing their respective advantages and limitations. By providing a comprehensive overview of these techniques, this paper aims to highlight the importance of accurate SNR estimation in improving SEM image quality and to present the latest advancements that make SNR estimation more efficient and applicable to a broader range of SEM imaging scenarios.

Keywords

• Signal-to-noise ratio (SNR)
• scanning electron microscope (SEM)
• scanning electron microscope (SEM) image
• image quality
• image noises
• single image