Deep ensemble neural network-driven modeling of etching process in advanced integrated circuit manufacturing

Abstract

Plasma etching plays an increasingly critical role in advanced integrated circuit manufacturing, making physical modeling more essential for mechanism analysis and precise control. However, physical etching models involve multiple empirical parameters that are difficult to extract, limiting their reliability and accuracy. Traditional modeling methods rely heavily on manual calibration through extensive design of experiments. This manual approach is inefficient, highly costly, and struggles to resolve complex, nonlinear physical effects in multidimensional parameter optimization. To address these challenges, this paper proposes an autonomous modeling method based on deep ensemble neural networks to construct a high-fidelity physical etching model. It can automatically extract and calibrate key model parameters using significantly less experimental data, to achieve predicted profiles that are in excellent agreement with experiments. The combined deep ensemble and Bayesian optimization framework balances exploration and exploitation, effectively overcoming local optima traps in complex, high-dimensional parameter spaces during the optimization process, demonstrating high accuracy, transferability, and efficiency. We validated this method through Si substrate etching experiments in Cl2 plasma, where the highly matched profiles confirmed the method’s precision. Furthermore, we extended the validation to a different trench dimension, demonstrating its promising transferability. Compared with the traditional gradient descent method, our approach achieves a 10.2× speedup with a lower loss value. This work establishes a scientific and highly efficient methodology for high-cost modeling of the etching process in advanced semiconductor manufacturing.