Dr. Sool PARK
Senior Manager, Data Science Applications Engineering, Lam Research
Sool Park is a Senior Manager of Data Science in the Customer Service Business Group (CSBG) at Lam Research, with 10 years of experience at the company. He leads the development and field implementation of EI DA (Equipment Intelligence Data Analyzer), a machine learning and big data infrastructure deployed inside customers’ fabs to enable scalable equipment intelligence.
His work supports Lam Research’s product portfolio, including etch, deposition, and spin clean technologies, and provides global customer support across logic and memory manufacturing. He is strongly driven by customer success, taking a practical, production focused approach to data science that emphasizes real manufacturing data and actual production patterns from customer sites rather than purely theoretical models.
His overarching objective at Lam Research is to establish a data driven decision making culture across all engineers, helping customers achieve better performance, consistency, and operational efficiency.
Presentation Title
AI Enabled Fleet Intelligence Driving Customer Success
As semiconductor manufacturing generates ever larger and more complex volumes of equipment data, Lam is leveraging AI—built on machine learning and advanced data analytics—to ingest, manage, and analyze large scale manufacturing data driven by real production patterns.
Today, Equipment Intelligence® Data Analyzer (EI DA) delivers fleet level analytics across Lam’s etch, deposition, and spin clean technologies. With scalable data ingestion and machine learning driven analysis, EI DA enables chamber to chamber comparisons, fleet matching, performance trending, and early identification of equipment and process variation in high volume manufacturing. Applied directly to production data, these capabilities help engineers move beyond theoretical analysis and focus on practical, production driven insights that support customer success.
A key strength of EI DA is its ability to deliver consistent, repeatable analysis across global customer sites by standardizing data collection, processing, and interpretation at the fleet level. By embedding analytics into everyday engineering workflows, EI DA supports faster troubleshooting, greater operational consistency, and measurable productivity gains.
While today’s implementations focus on fleet level analytics, the underlying architecture is designed to support increasingly sophisticated AI capabilities. EI DA is an AI driven foundation built to power predictive, automated, and adaptive decision making across the fab.