A Correlative Microscopic Workflow Powered by Artificial Intelligence to Accelerate Failure Analysis of Next-Generation Semiconductor Packages

Abstract Over the past decade, the semiconductor industry has increasingly focused on packaging innovations to improve device performance, power efficiency, and reduce manufacturing cost. The recent heterogeneous integration offers an attractive solution in advanced IC packaging because it enables the integration of diverse functional components, such as logic, memory, power modulator, sensor on a single package platform. However, the adoption of the emerging structures, materials and components in advanced packages has challenged the existing fault isolation and analysis techniques. One of the major challenges is the limited accessibility to defects because fault regions are often located deep within devices. Without high-accuracy positional information of a defect, physical cross-sectioning and FIB polishing may alter or destroy the evidence of root causes. A non-destructive microscopic approach is preferred to map defective sites and surrounding structures. However, this method is limited by spatial resolution, especially for analyzing novel submicron interconnects such as fine pitch microbumps, redistribution layers (RDLs), and hybrid bonds. In this paper, we report an AI powered correlative microscopic workflow, where non-destructive X-ray imaging, FIB polishing and high-resolution SEM analyzing techniques are combined to solve the accessibility problem. Because 3D X-ray imaging may take a larger fraction of the time span over the entire workflow, a deep-learning based reconstruction method was applied to accelerate data acquisition. Several next-generation packages, fan-out wafer-level package (FOWLP) and hybrid bonds with sub 10 µm pitch, were used as the test vehicles to demonstrate the workflow performance and efficiency.