ON THE USE OF COMPONENT STRUCTURAL CHARACTERISTICS FOR VOXEL SEGMENTATION IN SEMICON 3D IMAGES

Detecting defects buried inside chips is critical for failure analysis in semiconductor manufacturing. In this paper, we perform 3D voxel segmentation on 2.5D semicon chips to locate and identify defects that may be present in them. We integrate tree based Ensemble method with the Cascaded Anisotropic Convolutional Neural Networks to employ component structural characteristics of semicon 3D object in voxel segmentation process. We fabricate custom 2.5D chips purposely creating defective regions by using a specific fabrication and assembling process. Thereafter, use commercial 3D XRM tools for 3D imaging of these chips. We perform accurate 3D Object localization for each 3D x-ray scan by using a slice and fuse approach. Then, we perform voxel segmentation on logic die (integral component of semicon chip) to detect Cu-pillar, solder, and void regions (if any). The results show that we achieve state-of-theart voxel segmentation dice scores for all three sub-components.