Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features

TL;DR

This paper develops surrogate neural network models to optimize plasma enhanced atomic layer deposition in high aspect ratio features, significantly reducing experimental effort and accurately predicting process parameters.

Contribution

It introduces machine learning surrogate models trained on simulation data to predict saturation times and surface recombination effects in PEALD, enhancing process optimization.

Findings

Two experiments suffice to predict saturation times within 10%.

Surrogate model achieves 99% accuracy in identifying dominant surface recombination.

Method can be extended to atomic layer etching and complex nanostructures.

Abstract

In this work we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching, surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma-surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to atomic layer etching and more complex structures.