SEMI Strategic Materials Conference (SMC) Korea 2026

As the semiconductor industry navigates the challenges of dimensional and functional scaling in the artificial intelligence (AI) era, advanced materials science has emerged as a critical enabler for performance enhancement. Modern AI-centric architectures demand the integration of increasingly complex system-on-chip (SoC) designs with non-traditional material systems.

This work details a high-throughput methodology for the rapid down-selection and optimization of multi-element thin films, ensuring alignment with both physical and electrical key performance indicators (KPIs). Utilizing combinatorial physical vapor deposition (PVD) in conjunction with unit-cell electrical test vehicles, we systematically screen an expansive elemental compositional space to identify optimal candidates. Advanced machine learning (ML) algorithms are integrated into each phase of the development lifecycle—spanning precursor synthesis, process optimization, and heterogeneous integration—to satisfy the rigorous specifications of emerging device applications. The synergy between integrated materials engineering and AI-driven informatics significantly reduces the temporal gap between material discovery and device-level implementation.