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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.07886 |
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| _version_ | 1866914082448211968 |
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| author | Sim, K. S. Bukhori, I. Ong, D. C. Y. Gan, K. B. |
| author_facet | Sim, K. S. Bukhori, I. Ong, D. C. Y. Gan, K. B. |
| contents | Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signal-to-Noise Ratio in Scanning Electron Microscopy: A Comprehensive Review Sim, K. S. Bukhori, I. Ong, D. C. Y. Gan, K. B. Machine Learning Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field. |
| title | Signal-to-Noise Ratio in Scanning Electron Microscopy: A Comprehensive Review |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.07886 |