AI-based 3D Metrology and Defect Detection of HBMs in XRM Scans

Abstract

In this paper, we employ the latest developments in 3D semi-supervised learning to create cutting-edge deep learning models for 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductor scans. We illustrate our approach to locating the region of interest of High Bandwidth Memory (HBM) structures and their individual components and identifying various defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefits of contrastive learning in the data pre-selection for our detection model and multi-scale Mean-Teacher training paradigm in 3D semantic segmentation to achieve better performance compared to the state of the art. We also provide an objective comparison for metrology-based defect detection with a 3D classification network. Our extensive experiments have shown that our approach outperforms the state of the art by up to 16% on object detection and 7.8% on semantic segmentation. Our fully-automated custom metrology package shows a mean error of less than 2 [Formula: see text]m for key features such as bond line thickness and provides better defect detection performance than the direct 3D classification approach. Overall, our method achieves state-of-the-art performance and can be used to improve the accuracy and efficiency of a wide range of failure analysis applications in semiconductor manufacturing. Finally, we also increase the segmentation models flexibility and adaptability to new data. We propose a generic training strategy and a new loss function that reduces the training time by 60% and the required amount of data by 48% making the training process more efficient.