Abstract
We propose a nondestructive inline inspection and subsequent simple quantitative estimation of profiles of copper-filled through-silicon vias (Cu-TSVs) by viewing one-shot-images of Cu-TSVs with a nano-focus X-ray microscope. A nano-focus X-ray microscope with a resolution of 250 nm makes it possible to observe detailed views of φ5 μm × 50 μm copper pillars and small defects inside them. We use image processing and a supervised machine learning method to classify the void profiles. The voids are usually considered to have an indefinite shape. If rotationally symmetric features of the voids are recognized, the voids are modeled with rotationally symmetric structures and their positions in the Cu-TSVs’ pillar and their volumes are estimated with quantified dimensions. A cross-sectional view after focus ion beam (FIB) processing coincides well with the estimates of geometrical parameters. The classified results can be linked to analysis of mismatches in the conditions during the electro-deposition process.
