Accelerate Your 3D X-ray Failure Analysis by Deep Learning High Resolution Reconstruction

Abstract

Over the past decade, 3D X-ray technique has played a critical role in semiconductor package failure analysis (FA), primarily owing to its non-destructive nature and high resolution capability [1,2]. As novel complex IC packages soar in recent years [3,4], X-ray failure analysis faces increasing challenges in imaging new advanced packages because IC interconnects are more densely packed in larger platforms. It takes several hours to overnight to image fault regions at high resolution or the crucial details of a defect remain undetected. A high-productivity X-ray solution is required to substantially speed up data acquisition while maintaining image quality. In this paper, we propose a new deep learning high-resolution reconstruction (DLHRR) method, capable of speeding up data acquisition by at least a factor of four through the implementation of pretrained neural networks. We will demonstrate that DLHRR extracts signals from low-dose data more efficiently than the conventional Feldkamp-Davis-Kress (FDK) method, which is sensitive to noise and prone to the aliasing image artifacts. Several semiconductor packages and a commercial smartwatch battery module will be analyzed using the proposed technique. Up to 10x scan throughput improvement was demonstrated on a commercial IC package. Without the need of any additional X-ray beam-line hardware, the proposed method can provide a viable and affordable solution to turbocharge X-ray failure analysis.