How GlobalFoundries Takes AI from Pilot to Global Scale

2026-03-23 · Source: Big Data & AI News - EE Times · Field: Manufacturing & Industrial — Smart Manufacturing & Industry 4.0, Manufacturing Operations & Management · Depth: Intermediate, medium

Summary

GlobalFoundries' VP of digital manufacturing, Sujieth Vaasan, is leading a strategic shift in semiconductor manufacturing, moving artificial intelligence from isolated projects to a unifying operational layer across global fabs. This approach integrates process engineering, data science, and global operations to identify and scale AI applications that deliver real manufacturing value. Vaasan's team focuses on use cases that benefit from learning and prediction, particularly those generalizable across GlobalFoundries' diverse fabs in the U.S., Europe, and Asia. Key application domains include tool performance, material efficiency, defectivity, and production planning. The company employs a rigorous four-criteria proof-of-concept (PoC) process to ensure AI initiatives target clear pain points, have reliable data access, provide explainability, and possess a clear deployment path, preventing PoC proliferation and ensuring successful scaling.

Key takeaway

For CTOs and VPs of Engineering evaluating AI deployment in complex manufacturing, your focus should shift from isolated optimizations to globally scalable solutions. Prioritize AI initiatives that address clear manufacturing pain points, ensure data reliability, and offer explainability for engineer trust. Implement a rigorous proof-of-concept framework with a clear path to deployment to avoid stalled projects and accelerate the integration of AI as core factory infrastructure.

Key insights

AI in semiconductor manufacturing is shifting from niche tools to a unifying operational layer for global scalability.

Principles

• Not every problem is an AI problem.
• AI works best when close to the process.
• AI enhances, not replaces, human engineers.

Method

GlobalFoundries organizes AI applications by manufacturing domain and applies a four-criteria PoC process: clear pain point, reliable data, explainability, and clear deployment path, ensuring global scalability.

In practice

• Run models at the edge for inline defect prediction.
• Use generative AI for knowledge management.
• Integrate AI with manufacturing systems.

Topics

• AI in Semiconductor Manufacturing
• GlobalFoundries
• AI Deployment Strategy
• Edge AI
• Yield Optimization

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.