Demystifying Edge Cases in Advanced IC Packaging Inspection through Novel Explainable AI Metrics

Inspecting advanced packaging systems for integrated circuits presents difficulties due to the intricacy of contemporary semiconductor packages, which include small dimensions, dense structures, and diverse materials. The intricate nature of these packages necessitates unparalleled precision in identifying features such as microbumps and through-silicon vias (TSVs). Explainable AI (XAI) algorithms play a vital role in providing clarity in decision-making processes and facilitating the comprehension of anomaly detection. However, the challenge lies in the absence of standardized metrics to assess the reliability of XAI algorithms. This research endeavor aims to establish industry-standard metrics for evaluating XAI techniques in the physical inspection of advanced packaging systems, thereby enhancing transparency and dependability in complex semiconductor scenarios.