Gallium oxide (Ga2O3) is rationally expected to have a wide range of applications prospects in power electronics, solar-blind UV detectors, gas detectors, and other fields. Metal-organic chemical vapor deposition (MOCVD) is a key technology for achieving high-quality and large-scale film growth, but with complex fluid fields, temperature fields, component distribution, and chemical reaction processes, the process is akin to a black box. In this study, the computational fluid dynamics (CFD) approach and a complete reaction mechanism were used to model the chemical reaction and mass transport for gallium oxide film growth based on a vertical rotating disc reactor (RDR) MOCVD system. The effect of temperature on film growth was explored and analyzed. It was found that the growth temperature can be divided into four regions, respectively controlled by thermal flow field, reaction rate, component transport, and parasitic reaction, and there is a competitive relationship between them. In the component transport region, the influence of different process parameters such as pressure, flow rate, and rotational speed on the growth was investigated. The regulation of film uniformity is achieved by the coupled regulation of 5 pairs of MO source inlets and multiple process parameters. Combined with artificial intelligence techniques, an intelligent system for realizing flow field visualization, growth result prediction, process parameter optimization, and abnormal result tracking was proposed for the first time. It can provide systematic guidance for film growth.
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