Analyzing Foreign Investment Patterns in the U.S. Semiconductor Value Chain Using AI-Enabled Analytics: A Framework for Economic Security

This paper introduces an AI-enabled analytical framework for assessing foreign investment patterns within the US semiconductor value chain to address emerging economic security challenges. The semiconductor industry constitutes a critical technological foundation for economic and military capabilities, with investment patterns revealing strategic targeting of key value chain segments. The methodology incorporates multi-source data integration from regulatory filings, corporate registries, and technical documentation to establish a comprehensive investment database. Machine learning algorithms including graph neural networks (95.6% detection accuracy) and deep neural networks (94.8% accuracy) enable identification of coordinated investment strategies and ultimate beneficial ownership structures that remain below current regulatory screening thresholds. Analysis reveals significant temporal-spatial shifts in investment targeting with pronounced concentration in electronic design automation (875% increase 2010-2023) and specialized materials segments (17.2% annual growth 2020-2023). Entity-level analysis identifies 47 high-risk investors employing sophisticated investment strategies targeting critical supply chain nodes. The research demonstrates substantial concentration in 12 technology sub-segments where foreign investment exceeds 65% of total investment volume. Proposed policy frameworks include AI-enhanced CFIUS screening methodologies, calibrated risk-based intervention approaches, and international coordination mechanisms supporting semiconductor supply chain resilience while maintaining innovation ecosystems. This framework provides policymakers with data-driven capabilities for precision-targeted intervention minimizing economic disruption while addressing legitimate national security considerations.