Revolutionizing Component Traceability and Quality Assurance with AI

In this Podcast, Dr. Eyal Weiss, founder and CTO of Cybord, sheds light on the challenges faced by the electronics industry and how AI-driven technology is changing the game.

The difficulty encountered by the industry is a lack of capability to comprehensively inspect each component for authenticity and defects before their placement. Visually identifying components and understanding their various characteristics may seem impossible at first. This is mainly due to the vast diversity of components and the need to cross-reference them with online databases.

However, this is where the capability of big data and artificial intelligence emerges. Dr. Weiss clarifies that AI’s role isn’t just data organization; it’s about comprehending the nuances of each component. Cybord’s AI detects unique “fingerprints” left by the machine that packaged the component during manufacturing. Therefore, instead of attempting to identify hundreds of millions of component types, we focused solely on recognizing the few thousand machines responsible for packaging them. It’s about understanding that every component is unique and can be treated as such.

Traditional inspection services mainly focus on integrated circuits, leaving a void in inspecting components. Cybord’s solution lies in utilizing existing machine image systems and combining them with AI to analyze each component. This ensures individual component traceability and quality assessment on an exceptional scale.  Cybord’s AI interprets markings, date codes, and more, which are otherwise complex and diverse, making individual component-level traceability possible.

In an industry fixated on board-level failures, component-level failures are overlooked. Cybord’s approach disrupts this mindset, revealing that component failures are more common and costly than presumed. Dr. Weiss stresses the significance of component-level traceability in improving recalls’ accuracy and reducing unnecessary recalls.

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