Multiscale X-ray computed tomography of standard optical fibers

TL;DR

The paper addresses the challenge of non-destructively mapping the refractive-index profile of optical fibers in 3D by deploying a multiscale XCT framework that compares $\mu$CT, XRM, and nCT. It integrates AI-assisted segmentation to improve image analysis and reports on core, trench, and cladding geometries across standard fiber spans, highlighting the trade-offs between resolution and field of view. The findings show that $\mu$CT is suitable for long spans with low spatial resolution, XRM offers the best practical compromise for real-time fiber drawing QA, and nCT delivers the finest detail but is limited by small field of view, with phase-contrast considerations at synchrotron sources enhancing feature visibility. Together, these results demonstrate a path toward AI-enhanced, multiscale fiber characterization and manufacturing control with potential real-time feedback for quality improvement.

Abstract

Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, Xray 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, 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, 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 fitting the device's field of view and the spatial resolution needed for properly imaging the inner features of the fiber. 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.

Figures (9)

• Figure 1: Arrangement of optical fibers in the XRM system. a) Radiographic image of the optical fibers. b) Tomographic section.
• Figure 2: XRM tomography results. (a-c) False color 3D rendering of the STEP (a), GRIN (b), and SMF (c) samples, respectively. The color bars represent the grayscale intensity that grows according to the arrow's direction. The grayscale is in arbitrary units and ranges between 0 and 65535, that is $2^{16}-1$, as the images are in 16 bits. The value 0 corresponds to the white background. (d-f) Color plot of the single slices indicated by the dark squares in (a-c), respectively. (g-i) Surface plot of the regions of interest in (d-f) indicated by white squares.
• Figure 3: nCT analysis of the SMF taper sample. (a) Color plot of a single $xy$ slices. The color bars represent the grayscale intensity that grows according to the arrow's direction. (b) Surface plot of the region of interest in (a) indicated by the white square.
• Figure 4: Comparison between the $xy$ slices of the STEP (a,b), the GRIN (c,d), and the SMF (e,f) samples obtained via $\mu$CT and XRM, respectively.
• Figure 5: Comparison between manual and DL-assisted segmentation of the XRM image of the GRIN fiber. The purple and the blue areas refer to pixels whose associated gray values are above and below the segmentation threshold, respectively.