Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning

Abstract

Deep Learning is being widely used to identify and segment various structures in 2D and 3D scans in fields such as robotics and medical imaging. We leverage this exciting technology to train state-of-the-art models for 3D object detection and segmentation for various buried structures such as Through Silicon Vias (TSVs), memory bumps, and logic bumps. We show in detail how we fabricate our wafers and generate 3D scans. Thereafter, we explain our approach in locating these different structures in 3D scans and how we further segment these structures into solders, voids, Cu-Pillars, and Cu-Pads for 3D metrology and defect identification. We compare our approach with state-of-the-art techniques and perform a thorough analysis to discuss the advantages and disadvantages of various approaches in each step.