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Increasing AI reliability for automotive chip production

A partnership between two automotive companies has developed and presented a case study to improve the robustness and reliability of AI solutions for use in automotive systems.

At the Fab Owners Alliance (FOA) Q1 Collaborative Forum in the US, Lynceus and NXP Semiconductors presented a case study entitled: “Qualifying AI for Automotive Production.” The presentation covered the importance of qualifying AI for production deployment, specifically for automotive-grade production.

David Meyer, CEO of Lynceus, comments: “AI solutions have not met the robustness and reliability standards of automotive-grade production, and the industry is missing a qualification framework to validate an AI solution that is ready for deployment in an automotive fab. In collaboration with NXP, we are the first to fully qualify AI for automotive-grade production. We have developed an actionable framework that can be repurposed for any AI application.”

The shift towards electric vehicles and autonomous systems means cars are increasingly packed with highly critical electronic components. Automotive chip manufacturers need to develop systems to reliably produce and optimise asset spending in a challenging market environment.

AI can, in principle, help improve yield and production capacity in existing semiconductor fabrication plants (fab), but this has not seen widespread adoption. The industry misses a robust qualification and introduction process, able to bridge the gap between a performant predictive model and a fully functioning AI production solution.

The presentation outlined a four-step strategy that covered personnel, reliability, infrastructure and manufacturing risks and opportunities. “Successfully deploying AI in a high-value manufacturing environment, such as a semiconductor fab, by focusing on predictive performance alone is not enough,” Meyer stated. “Building a comprehensive qualification case evidencing the expected impact on manufacturing performance, the robustness of the system and the feasibility of scaling is a critical and often neglected step to fostering large-scale adoption of AI.”

FOA members were the first to learn of the advantages of Lynceus’ AI implementation and can now adopt the technology for each unique purpose. Members also reaped the value from this first use case and can apply learned lessons from this successful partnership.

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