Multiscale and data-driven analysis of chemical vapor deposition processes

Chemical vapor deposition (CVD) is a key manufacturing technique widely used in the semiconductor, energy, and advanced materials industries to produce thin films with controlled composition and microstructure. However, the underlying mechanisms governing CVD processes are inherently complex because they involve phenomena occurring across multiple spatial and temporal scales, including gas-phase reactions, transport processes, and surface chemistry. In this study, the interplay between these multiscale effects and the role of operating conditions in determining film growth behavior are systematically investigated. A multiscale computational modeling framework is developed to couple reactor-scale transport phenomena with surface reaction kinetics, enabling a more comprehensive understanding of deposition dynamics. In addition, data-driven analysis methods are employed to identify patterns and correlations between process parameters and resulting film properties. The computational results are validated using experimental measurements, providing insights into how temperature, pressure, and precursor concentrations influence deposition rates and film uniformity. This combined modeling and data-driven approach offers a pathway toward improved prediction, optimization, and control of CVD processes in advanced material fabrication.