AI-Driven Parameters Optimization of Smooth Vertical Sidewall Trench in DRIE Process Based on Neural Network

Deep Reactive Ion Etching (DRIE) is a critical process in the fabrication of microelectromechanical systems (MEMS) and other advanced semiconductor devices. Achieving the desired trench profile with minimal defects remains a significant challenge due to the complex interplay of various process parameters. This paper focuses on optimizing parameters such as etching cycle time and passivation cycle time in the DRIE process. An improved central composite design method was employed to design 18 sets of etching recipe experiments. Actual etching profile data collection was conducted, resulting in 1800 sets of etching profile data, which serve as the foundation for subsequent AI algorithm optimization. Through real data experiments, effective etching recipes for high-precision smooth vertical sidewalls were identified. It was found that an etching/passivation ratio in the range of 1.09 to 1.4 could achieve 90-degree sidewalls, while a ratio between 1.09 and 1.5 could achieve scallop heights below 90 nm. Furthermore, based on convolutional neural networks and data-driven models, the predictive models are proposed for etching profile angles and scallop smoothness. The results demonstrated a high prediction accuracy, with only a 5.5% error in the etching profile angle prediction. The optimized etching results and recipes identified in this study achieve highly smooth sidewalls. This work not only enhances the quality of etching processes but also contributes significantly to the advancement of AI applications in semiconductor manufacturing.