edfas.org ELECTRONIC DEVICE FAILURE ANALYSIS | VOLUME 28 NO.4 4 EDFAAO (2026) 4:4-11 1537-0755/$19.00 ©ASM International® ADVANCED PACKAGING FAILURE ANALYSIS: UNLEASHING AI’S FULL POTENTIAL Sam Lin Thermo Fisher Scientific, Hillsboro, Oregon sam.lin@thermofisher.com INTRODUCTION Artificial intelligence (AI) accelerators and high-performance computing (HPC) processors demand extreme memory bandwidth, ultra-low latency, and exceptional power efficiency. These requirements have fundamentally reshaped semiconductor system design, driving the widespread adoption of advanced packaging technologies, particularly 2.5D interposers with high-bandwidth memory (HBM), 3D die stacking, and heterogeneous chiplet integration. As highlighted at the 2022 TSMC Open Innovation Platform (OIP) for its 3DFabric Alliance, the future of AI performance scaling is no longer determined by transistor density alone (Fig. 1).[1] Instead, TSMC’s 3DFabric Alliance defines a tightly integrated ecosystem spanning design, manufacturing, materials, equipment, and testing, with the objective of accelerating innovation while ensuring manufacturability and long-term reliability. In this new paradigm, system-level performance and reliability increasingly depend on the quality and integrity of advanced packaging structures, including microbumps, redistribution layers (RDLs), through-silicon vias (TSVs), and hybrid bonding interfaces. Together, these structures form the critical electrical, thermal, and mechanical pathways that enable massive data transfer between logic and memory, which is essential for AI training and inference workloads. Within this ecosystem, FA and advanced characterization technologies play a foundational role. For AI devices, even a single latent packaging defect, such as a void in a microbump, a discontinuity in an RDL, a TSV defect, or underfill delamination, can result in bandwidth throttling, increased power consumption, thermal instability, or early life failure in the field. As a result, packaging-related de- fects have emerged as one of the dom- inant yield and reliability limiters for advanced AI hardware. The success of the 3DFabric Alliance therefore depends not only on advanced process integration, but also on the ability to rapidly detect, localize, and understand packaging defects across multiple length scales, enabling faster learning cycles, improved yield ramp, and robust field reliability. HOW FAILURE ANALYSIS REVEALS PACKAGING-BURIED DEFECTS Compared with conventional consumer electronics, AI accelerators and HPC devices operate under significantly more aggressive electrical, thermal, and mechanical conditions, which fundamentally changes the nature of packaging-related failures. Key stress factors include: • Higher current densities in microbumps and RDLs, accelerating electromigration and void formation. • Sustained high power dissipation, which amplifies thermomechanical stress, package warpage, and interfacial fatigue. • Tight timing margins, making AI devices highly sensitive to even minor signal and power integrity degradation. • Large package form factors (e.g., HBM-based accelerators), increasing the risk of underfill delamination, die cracking, and interposer fracture. Fig. 1 TSMC 3DFabric Alliance announced at the Open Innovation Platform Ecosystem Forum 2022.
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