Bridging Plasma Equipment and Surface Processes: Insights from Simulation and AI

Semiconductor manufacturing continually faces challenges from device miniaturization and increasing process complexities. Addressing these issues requires precise control and understanding of plasma-driven surface interactions. This talk highlights the role of computational science and artificial intelligence in bridging the gap between plasma equipment conditions and surface processing outcomes. By employing multiscale simulation techniques, including Molecular Dynamics and Density Functional Theory, detailed insights into atomistic-level surface interactions under varying plasma conditions are achieved. Scale-bridging methodologies translate these microscopic insights to macroscopic wafer-scale behaviors, elucidating the intricate relationships between equipment-level plasma parameters and resulting material responses. Furthermore, integrating these simulation-derived insights with advanced machine learning techniques enhances predictive accuracy and process control, ultimately driving innovation in semiconductor manufacturing. Practical examples demonstrating the synergy between simulation and AI methodologies will highlight their transformative potential for optimizing plasma-based processing technologies. In this talk, challenges and opportunities will be discussed to bridge the gap between machine-level and process perspectives, a critical step in shaping practical methods to access semiconductor processes from a theoretical standpoint.