Enhancing u.s. Semiconductor sovereignty: a predictive maintenance framework for minimizing industrial downtime in domestic fabrication plants

Abstract

The United States semiconductor manufacturing sector faces critical challenges in maintaining competitive operational efficiency while rebuilding domestic production capacity amid global supply chain uncertainties. Equipment downtime in semiconductor fabrication facilities directly threatens production targets, increases manufacturing costs, and undermines national strategic objectives for technological self-sufficiency. This research presents the design, implementation, and validation of a predictive maintenance framework specifically engineered to minimize unplanned downtime in domestic semiconductor fabrication plants. The proposed framework integrates industrial IoT sensors for continuous equipment monitoring, advanced analytics for failure prediction, machine learning algorithms for maintenance optimization, and automated work order generation systems. Through deployment across two U.S. semiconductor fabrication facilities producing logic and memory chips with combined annual revenue of $4.2 billion, the system demonstrated substantial operational improvements over traditional time-based maintenance approaches. Results show 87% accuracy in predicting equipment failures 24-72 hours in advance, 64% reduction in unplanned downtime, 41% decrease in maintenance costs, 28% improvement in overall equipment effectiveness (OEE), and estimated $127 million annual productivity gain across both facilities. The research contributes both a comprehensive predictive maintenance architecture addressing semiconductor-specific requirements and empirical evidence demonstrating practical feasibility and economic benefits. Findings indicate that intelligent maintenance strategies represent critical enablers for U.S. semiconductor sovereignty, allowing domestic facilities to achieve competitive efficiency levels essential for long-term viability.