Etching process prediction based on cascade recurrent neural network

Abstract

Etching is one of the most critical processes in semiconductor manufacturing. Etch models have been developed to reveal the underlying etch mechanisms, which employs rigorous physical and chemical process simulation. Traditional simulation is very time consuming. The data-driven artificial intelligence model provides an alternative modeling approach. In this paper, a Cascade Recurrent Neural Networks (CRNN) is proposed to model and predict etching profiles. The etching profile is represented by polar coordinates and modeled by the recurrent neural networks, the corresponding etching parameters (e.g., pressure, power, temperature, and voltage) are integrated into the network through cascade combination layers. Experimental results on a dataset of 10,000 simulated etching profiles demonstrated the effectiveness of our method: compared with traditional etching simulation methods, CRNN can speedup 21,000$\times$ with an average error of less than 0.7 nm for 1 step prediction. Furthermore, compared to simple deep neural networks, the Mean Absolute Errors (MAE) could be reduced from 1.7329 nm to 1.3845 nm for 10 steps prediction. Finally, the effectiveness and accuracy of CRNN etching predictor is validated through fine-tuning on experimental data.