Secure Hardware Assurance Using Visual AI on AOI Imaging of Electronic Assemblies

Ensuring the authenticity and integrity of electronic assemblies is increasingly critical as hardware-based attacks and unauthorized component modifications become more sophisticated. Conventional inspection systems, whether rule-based AOI or traceability logs, offer limited protection against subtle or intentional tampering.This paper introduces a deep learning–based framework for secure hardware assurance that operates directly on AOI image data, enabling autonomous, full-coverage verification of every component on the board. The method is built on two previously patented systems: one for component authentication via visual fingerprinting—independent of top marking—and another for contextual decoding of top marking codes. These systems have been deployed across tens of SMT lines, generating over 5 billion production-grade inspections.By integrating and extending these capabilities, the system performs bottom-up part analysis and top-down layout validation, identifying substitutions, rework, and tampering, without requiring electrical probing, golden boards, or metadata.Results show > 99% detection accuracy and sub-second inspection times, enabling secure, image-only verification suitable for in-line or forensic use. This work bridges the gap between software protections and physical hardware trust, transforming AOI images into a practical security and compliance enforcement tool.