Abstract
<jats:p>Authentic, trusted semiconductor components are essential for safety-critical systems, but traditional manual, documentation-based verification cannot keep pace with complex, globalized chip supply chains and increasingly sophisticated counterfeit, recycled, and remarked ICs. AI-based technologies address this gap by detecting subtle counterfeit indicators and hidden patterns in supply chain data, focusing particularly on cases where conventional methods fail to flag suspicious parts. In this context, AI-encoded systems support semiconductor authenticity through three main capabilities: vision-based inspection for chip and packaging features, AI-generated data for origin and provenance verification, and AI-driven evaluation for smart trust scoring across suppliers and lots. These approaches enable automated chip inspection and anomaly detection in supply chain behavior, and their performance can be directly compared with traditional non-AI verification to demonstrate gains in accuracy, coverage, and reliability.</jats:p>