Study on stability of cavity in metal-organic chemical vapor deposition calculation based on neural network method

The computational fluid dynamics (CFD) method is widely used to study the process parameters and internal flow states of reactor chambers based on metal-organic chemical vapor deposition (MOCVD) to guide film growth. Currently, several machine learning models have been used in CFD studies, and the prediction accuracy of such models is positively correlated with the amount of data. Thus, two-dimensional (2D) models are used in CFD studies, while three-dimensional (3D) models contain more information and have been used more widely. Herein, neural network (NN) models for target regions based on a 3D MOCVD reactor are proposed and applied to flow-stability studies using the MOCVD reactor chamber. NN models are used to predict the cavity stability curve, and the range of process parameters can be controlled by the characteristics of the curve. NN prediction results have higher accuracy, after the model is established, which considerably reduces the work of CFD numerical simulation and lays a foundation for MOCVD equipment design and process debugging. Published under an exclusive license by AIP Publishing.

Automated summary

The computational fluid dynamics (CFD) method is used to study the process parameters and internal flow states of reactor chambers based on metal-organic chemical vapor deposition (MOCVD). Neural network (NN) models based on a 3D MOCVD reactor are proposed to predict cavity stability and control process parameters. These models have high accuracy, reduce the workload of CFD numerical simulation, and are significant for MOCVD equipment design and process debugging.