Optimizing Plasma Etching: Integrating Precise Three-Dimensional Etching Simulation and Machine Learning for Multi-Objective Optimization

Abstract

In modern semiconductor manufacturing, optimizing plasma etching processes is key for advancing technology and achieving profitable production. In this study, we propose an efficient process flow to calibrate the etching mechanisms and optimize the etch profile by combining three-dimensional plasma simulations with machine learning (ML). Our proposed workflow speeds up the model calibration process and effectively addresses data scarcity issues by integrating extensive simulation data with TEM and other data sources. We have developed a three-dimensional Cl2/HBr/O2 plasma etching model for shallow trench isolation (STI), which is based on physical and chemical reaction mechanisms and external parameters consistent with actual production requirements, such as gas ratio, time duration, and chamber conditions. Machine learning is employed to tackle both forward and inverse problems. In the forward problem, the model predicts etching profiles under varying process conditions, with accuracy and similarity assessed using multiple criteria, including the structural similarity index (SSIM). Our approach achieves a notable reduction in prediction time, completing forecasts in 0.5 seconds compared to the 6500 CPU seconds required by TCAD. In the inverse problem, the model adjusts physical parameters to match observed results, improving its predictive accuracy. After model calibration, predictions are validated with wafer test outcomes, showing relative errors below 7%, thereby confirming the accuracy and robustness of the model parameters and the effectiveness of the entire process.

Keywords

• Plasma etch
• 3D etching simulation
• neural networks
• shallow trench isolation (STI)
• machine learning
• multiple-input multiple-output