AI-Driven Non-Destructive Characterization for Advanced Packaging: Challenges, Frameworks, and Opportunities

The scaling limits of CMOS technology and the rise of heterogeneous integration have intensified the demand for advanced packaging architectures. While these approaches enable unprecedented improvements in performance, energy efficiency, and system reliability, they also introduce new challenges in inspection, assurance, and failure analysis. Inspection becomes increasingly difficult as fine-pitch interconnects, buried interfaces, and stacked structures obscure critical features and generate imaging artifacts. Assurance is complicated by the globalized supply chain and the need to verify raw material provenance, process consistency, and design compatibility against evolving reliability requirements. Failure analysis faces its own set of obstacles, as conventional cross-sectioning and electrical probing are often destructive, labor-intensive, and incapable of resolving subtle defects like voids, delamination, or fatigue cracks at the micro- and nanoscale. Non-destructive modalities such as X-ray microscopy, scanning acoustic microscopy (SAM), and terahertz time-domain spectroscopy (THz-TDS) offer complementary capabilities for imaging deeply buried structures and assessing material integrity. However, the heterogeneous material stacks, noise artifacts, and resolution constraints inherent in these modalities necessitate systematic frameworks for characterization and inspection-aware design. This work presents a research agenda to confront these challenges through three interconnected efforts. First, we propose a security framework for raw materials and packaging assurance, defining provenance-based metrics and multimodal characterization methods to ensure material integrity and mitigate supply chain vulnerabilities. Second, we introduce NEMS/MEMS-enabled interposer concepts as a hardware-level safeguard, embedding reconfigurable structures that deter intellectual property theft, recycling, and reverse engineering while supporting reliable, low-power interconnects. Third, we advance design-for-inspection methodologies, which co-optimize packaging architectures with imaging parameters to enhance defect visibility, improve inspection efficiency, and enable integration into electronic design automation workflows. Building on the laboratory’s suite of destructive and non-destructive inspection capabilities, this agenda also incorporates AI-driven assistants for X-ray and multimodal data interpretation to address challenges of complex inspection processes, alignment and imaging inconsistencies, and dependence on manual review. Taken together, these strategies address assurance from the material, structural, and design perspectives, positioning AI-guided, inspection-aware approaches as a foundation for secure and trustworthy advanced packaging.