Exploring TMA and H2O Flow Rate Effects on Al2O3 Thin Film Deposition by Thermal ALD: Insights from Zero-Dimensional Modeling

This study investigates the impact of vapour-phase precursor flow rates—specifically those of trimethylaluminum (TMA) and deionized water (H2O)—on the deposition of aluminum oxide (Al2O3) thin films through atomic layer deposition (ALD). It explores how these flow rates influence film growth kinetics and surface reactions, which are critical components of the ALD process. The research combines experimental techniques with a zero-dimensional theoretical model, designed specifically to simulate the deposition dynamics. This model integrates factors such as surface reactions and gas partial pressures within the ALD chamber. Experimentally, Al2O3 films were deposited at varied TMA and H2O flow rates, with system conductance guiding these rates across different temperature settings. Film properties were rigorously assessed using optical reflectance methods and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The experimental findings revealed a pronounced correlation between precursor flow rates and film growth. Specifically, at 150 °C, film thickness reached saturation at a TMA flow rate of 60 sccm, while at 200 °C, thickness peaked and then declined with increasing TMA flow above this rate. Notably, higher temperatures generally resulted in thinner films due to increased desorption rates, whereas higher water flow rates consistently produced thicker films, emphasizing the critical role of water vapour in facilitating surface reactions. This integrative approach not only deepens the understanding of deposition mechanics, particularly highlighting how variations in precursor flow rates distinctly affect the process, but also significantly advances operational parameters for ALD. These insights are invaluable for enhancing the application of ALD technologies across diverse sectors, including microelectronics, photovoltaics, and biomedical coatings, effectively bridging the gap between theoretical predictions and empirical results.