Fully convolutional networks for void segmentation in X-ray images of solder joints. (September 2020)

• Record Type: Journal Article
• Authors: Wankerl, Heribert; Stern, Maike Lorena; Altieri-Weimar, Paola; Al-Baddai, Saad; Lang, Kurt-Jürgen; Roider, Florian; Lang, Elmar Wolfgang
• Is Part Of: Journal of manufacturing processes. Volume 57(2020)
• Page Start: 762
• Page End: 767
• Publication Date: 2020-09
• Subjects: Deep learning -- Fully convolutional networks -- Image segmentation -- Transfer learning -- Surface-mounted technology -- Voids; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.07.021
• Languages: English
• ISSNs: 1526-6125

Abstract

Highlights: A method for void and solder joint segmentation in X-ray images is suggested. Segmentation in the presence of brightness fluctuations and overlapping structures. Transfer learning and image gradients enhance the ability to generalize and adapt. The establishment of a library of components enables automatic inline void inspection. Abstract: Whether in sensory or illumination applications, optoelectronic components are an essential part of our everyday life. To actually include them in smartphones, cars or other products, they need to be soldered onto the surface of a printed circuit board via surface-mounted technology. Hereby, the solder joint is formed by remelting the solder paste that was printed onto the board before the optical component was mounted. During this process, the evaporating flux causes porosities (voids) filled with gas that is caught in the solidifying alloy. Voids influence the thermal and electric properties of a solder joint and hence reduce its reliability. Because the solder joint is embedded between the device and the board, non-destructive X-ray inspection is used to visualize voids. However, the superposition of various structures in a noisy image acquisition process renders the semantic segmentation of solder joints and voids in X-ray images difficult. To the best of our knowledge, there is no method for automatic void segmentation in flat solder joints based on X-ray images aside from this work. We develop a fully convolutional network for pixel-wise classification of X-ray images and show, how our contributions enable automatic void inspection of soldered structures without protracted X-ray tomography of flat samples, so-called laminography.