Moving beyond the AI pilot stage and enabling AI’s adoption across the entire design and manufacturing semiconductor supply chain at scale.
Abstract:
Semiconductor manufacturers face a mounting data crisis: modern fabrication facilities generate petabytes of complex, siloed data, yet less than 5% of it is typically used in analytics. Traditional business intelligence tools lack the scalability to handle datasets with millions of parameters, leaving critical yield and quality insights untapped. In this presentation we outline a comprehensive strategy to address this challenge through an integrated data, analytics and AI platform, which combines four key pillars: scalable parallel and distributed analytics architecture delivering approximately 25-fold performance improvements; enterprise ModelOps for managing AI model lifecycles at scale; a semiconductor-specific semantic data layer that breaks down silos across yield, design, process, and equipment data; and an agentic LLM integration built on a “Semantic, Agentic, and Secure” framework. Central to the approach is treating workflows as the system’s internal language; serving as long-term memory, domain guardrails, and reusable playbooks to ensure transparency and prevent AI hallucinations. With an air-gapped, on-premises LLM option for IP-sensitive environments, this platform is designed to de-risk AI adoption while maximizing the return on legacy data investments. In our opinion, this type of platform is critical to move beyond the AI pilot stage and enable adoption of AI across the entire design and manufacturing semiconductor supply chain at scale.
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