Lingua non specificata: Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, X-ray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of standard optical fibers. We carry out an intercomparison of three tomography setups: classical computed microtomography, X-ray microscopy, and nanotomography. In each method, our analysis highlights the trade-offs between resolution, field of view, and segmentation efficiency. Additionally, we integrate deep learning segmentation thresholding to improve the image analysis process. Thanks to its large field of view (10 × 10 mm2), microtomography with classical sources is ideal for the analysis of relatively long fiber spans, where a low spatial resolution is acceptable. The other way around, nanotomography has the highest spatial resolution (50–150 nm), but it is limited to very small fiber samples, e.g., fiber tapers and nanofibers, which have diameters of the order of a few microns. Finally, X-ray microscopy provides a good compromise between the sample size (of the order of 1 mm) fitting the device’s field of view and the spatial resolution needed for properly imaging the inner features of the fiber (about ). Specifically, thanks to its practicality in terms of costs and cumbersomeness, we foresee that the latter will provide the most suitable choice for the quality control of fiber drawing in real-time, e.g., using the ”One-Minute Tomographies with Fast Acquisition Scanning Technology” developed by Zeiss. In this regard, the combination of X-ray computed tomography and artificial intelligence-driven enhancements is poised to revolutionize fiber characterization, by enabling precise monitoring and adaptive control in fiber manufacturing (such as fiber size and non-circularity).
Multiscale X-ray computed tomography of standard optical fibers
Crocco, M. C.;Siprova, S.Membro del Collaboration Group
;Barberi, R. C.Conceptualization
;Agostino, R. G.Conceptualization
;Termine, R.Validation
;Bravin, A.Methodology
;
2025-01-01
Abstract
Lingua non specificata: Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, X-ray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of standard optical fibers. We carry out an intercomparison of three tomography setups: classical computed microtomography, X-ray microscopy, and nanotomography. In each method, our analysis highlights the trade-offs between resolution, field of view, and segmentation efficiency. Additionally, we integrate deep learning segmentation thresholding to improve the image analysis process. Thanks to its large field of view (10 × 10 mm2), microtomography with classical sources is ideal for the analysis of relatively long fiber spans, where a low spatial resolution is acceptable. The other way around, nanotomography has the highest spatial resolution (50–150 nm), but it is limited to very small fiber samples, e.g., fiber tapers and nanofibers, which have diameters of the order of a few microns. Finally, X-ray microscopy provides a good compromise between the sample size (of the order of 1 mm) fitting the device’s field of view and the spatial resolution needed for properly imaging the inner features of the fiber (about ). Specifically, thanks to its practicality in terms of costs and cumbersomeness, we foresee that the latter will provide the most suitable choice for the quality control of fiber drawing in real-time, e.g., using the ”One-Minute Tomographies with Fast Acquisition Scanning Technology” developed by Zeiss. In this regard, the combination of X-ray computed tomography and artificial intelligence-driven enhancements is poised to revolutionize fiber characterization, by enabling precise monitoring and adaptive control in fiber manufacturing (such as fiber size and non-circularity).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
