3D Defect Detection and Metrology of HBMs using Semi-Supervised Deep Learning

Abstract

3D Deep Learning has made tremendous progress recently and is being widely used in various fields, such as medical imaging, robotics, and autonomous vehicle driving, to identify and segment various structures. In this work, we leverage the recent developments in 3D semi-supervised learning to develop state-of-the-art models for 3D object detection and segmentation for various buried structures such as memory and logic die. We briefly describe our approach to fabricating, generating 3D scans, and annotating these samples. Thereafter, we explain our approach to locating these buried structures by demonstrating how semi-supervised learning is adopted to leverage vast amounts of available unlabeled data to improve both detection and segmentation performance. We also develop a metrology package that performs post-processing and outputs various important metrics for each package, such as void-to-solder ratio, pad misalignment, solder extrusion, and bond line thickness. Overall, we observe an improvement of up to 16% in object detection and 6% in 3D segmentation. Our final metrology results show a mean error of less than 1.24um for BLT and 0.753um for Pad misalignment when compared to the ground-truth labeled data.