The semiconductor industry is undergoing rapid transformation driven by increasing process complexity, global competition, and the need for continuous innovation. As fabs strive to maximize productivity and tool uptime, they are simultaneously grappling with persistent challenges: widening workforce skill gaps, loss of tribal knowledge as experienced experts retire, slow and manual issue resolution processes, and the need to improve Overall Equipment Effectiveness (OEE). Traditional systems and static documentation are no longer sufficient to support the fast-paced and highly technical environment of modern semiconductor manufacturing. Smart Manufacturing demands intelligent, adaptive systems that deliver real-time insights and decision support directly within operational workflows. Generative AI (GenAI), powered by Large Language Models (LLMs), is emerging as a key enabler in this transformation. By synthesizing knowledge across vast datasets and providing contextual, conversational access to insights, GenAI Virtual Assistants are helping to significantly improve workforce productivity, reduce Mean Time to Repair (MTTR), and drive faster, data-informed decisions on the fab floor and at the equipment level. This session will present real-world use cases demonstrating the impact of GenAI Virtual Assistants and an End-to-End GenAI platform deployed at both equipment makers and fabs. For equipment makers, we will show how Field Service Engineers and R&D teams use GenAI to accelerate time from lab to fab, improve tool availability, and deliver faster, more effective support—resulting in enhanced customer satisfaction and operational efficiency. At the fab level, we will explore how GenAI Virtual Assistants empower operators, technicians, and engineers by providing immediate access to troubleshooting knowledge, step-by-step guidance, and cross-tool, cross-region intelligence—dramatically reducing MTTR and enabling continuous upskilling of the workforce. By capturing and operationalizing expert knowledge, GenAI helps standardize best practices and ensures that critical insights are preserved and reused across the organization. Implementing GenAI Virtual Assistants requires a robust and scalable platform capable of: • Integrating structured and unstructured data from diverse sources. • Contextualizing and stitching data across domains (tools, process, maintenance, etc.). • Offering lightweight, customizable application development for rapid deployment. • Providing a knowledge workbench to curate and operationalize domain expertise. • Enabling automation workflows that improve efficiency and consistency. The presentation will outline the architectural components of such a platform and highlight the critical role of human-AI collaboration in achieving sustainable value. We will also discuss how this approach can be extended to other domains—such as process optimization, quality assurance, and yield improvement—enabling a broader transition toward truly autonomous Smart Manufacturing environments. By combining GenAI with deep domain knowledge, fabs and equipment makers can unlock new levels of agility, productivity, and innovation—empowering the next generation of semiconductor manufacturing.
