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    Home » Stop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AI

    Stop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AI

    Team_NationalNewsBriefBy Team_NationalNewsBriefAugust 21, 2026 Technology No Comments2 Mins Read
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    About this Webinar

    Turn Yield Excursions into Faster, More Confident Root Cause Analysis

    When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act on.

    What You’ll Learn:

    Discover how a purpose-built semiconductor analytics platform can help engineers connect insights across domains without moving data. See how Agentic AI, semiconductor-specific visualizations, and push-down compute enable faster investigation of yield excursions and process issues, even across billions of data points. In the session, a live demonstration shows how to conduct a multi-domain root cause investigation using Spotfire® Industry Pro.

    Key Takeaways:

    • Understand why siloed manufacturing data delays yield recovery and inflates costs
    • Learn how Agentic AI automates complex cross-domain analytics and visualization generation
    • Explore methods for scaling high-performance analytics across massive fab datasets

    Who Should Attend:

    Yield, Process, and Integration Engineers; Fab and Manufacturing Operations Managers; Quality and Reliability Engineers; and Data & Analytics leaders supporting wafer fabs, foundries, OSATs, and IDMs who need to identify issues faster while maintaining confidence in decision-making.

    Save Your Spot!

    Join this webinar to learn how leading semiconductor teams are accelerating root cause investigations, scaling analytics across massive datasets, and transforming disconnected data into actionable manufacturing intelligence. Reserve your seat today.

    Register now for this free webinar!



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