Even in advanced semiconductor fabs, Sub-Fab and cleanroom vacuum systems often rely on run-to-failure or scheduled preventive maintenance, resulting in inefficiencies, excess energy consumption, increased labor demands, and unnecessary spare parts usage.

Edwards, a global leader in vacuum technology, addresses these challenges with a Predictive Maintenance (PdM) model powered by Machine Learning (ML). By making thousands of pumps and abatement systems intelligent, PdM enables data-driven, condition-based maintenance tailored to Sub-Fab operations.

Abatement systems, essential for reducing emissions, require frequent servicing. Edwards’ ML algorithm — applied to over 500 maintenance events — revealed that nearly 50% of preventive maintenance actions were unnecessary. AI-driven insights help optimize service timing, reducing resource usage and improving system uptime.

In dry pump applications under harsh CVD conditions, PdM analysis showed that only 35% of pump swaps were timely, 20% could have prevented unscheduled downtime, and 45% were performed too early — highlighting the value of predictive, condition-based strategies.

Edwards leads the industry in applying ML to vacuum systems by transforming deep domain expertise into predictive models. These AI solutions enhance fab operational efficiency, lower costs and energy consumption, and help address future workforce constraints.