Electronic Components Provenance Assurance Through AI and Big Data in Assembly Processes

This article presents a groundbreaking method for real-time authentication of electronic components during the assembly process. Leveraging images captured by pick-and-place machines, our approach utilizes artificial intelligence (AI) and big data algorithms to discern subtle differences in component appearances, allowing for the identification of specific manufacturing lines and authentic manufacturers. By training our algorithm on a vast dataset of over four billion components, we achieve exceptional performance parameters in most cases exceeding 99.8%, contingent upon the package case of the component. Real-life examples illustrate instances of authenticity mismatches encountered in production, highlighting the method’s effectiveness in identifying errors in the supply chain and detecting counterfeit components. Through cross-referencing with top-side images and supply chain documentation, we validate the results, establishing a new standard for authenticity assurance without reliance on traditional methods. This methodology represents a true AI revolution, promising enhanced supply chain integrity, cost reduction, and mitigation of risks associated with counterfeit components.