From System to Package to Interconnect: An Artificial Intelligence Powered 3D X-ray Imaging Solution for Semiconductor Package Structural Analysis and Correlative Microscopic Failure Analysis

Abstract

Non-destructive 3D X-ray microscopy (XRM) has played a crucial role in fueling the advances of IC package development and failure analysis [1]-[2]. Over the past decade, the industry has increasingly focused on packaging innovations to improve device performance. The emergence of numerous new 2.5D, 3D and recent heterogenous integration packages challenges the existing X-ray imaging and analysis techniques because IC interconnects are more densely packed in larger and thicker packages. It takes several hours or longer for a 3D X-ray scanner to acquire high resolution and quality images of fine-pitch interconnects and fault regions. In this report, we will introduce a deep learning high-resolution reconstruction (DLHRR) method through the implementation of trained neutral networks capable of improving scan speed by a factor of four. To demonstrate the effectiveness of this new method applied to the packaging hierarchy, an intact smartphone, several component modules, and embedded interconnectors will be imaged and reconstructed with the DLHRR method. With the improved efficiency of the AI powered X-ray imaging technique, a correlated fs-laser/FIB-SEM workflow followed to precisely target and analyze the deeply buried defects, which has been difficult, if not impossible, for conventional package FA techniques. We will discuss the DLHRR method and applications in two following workflows: X-ray imaging workflow for package structural analysis, and correlative X-ray and fs-laser/FIB-SEM workflow for package failure analysis.