Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features
This work addresses optimization challenges in microelectronics manufacturing by accelerating PEALD processes, though it is incremental as it applies existing machine learning methods to a specific domain problem.
The researchers tackled the problem of long exposure times needed for full conformality in plasma enhanced atomic layer deposition (PEALD) within high aspect ratio features by using surrogate models based on artificial neural networks, achieving predictions of saturation times within 10% of ground truth from just two experiments and 99% accuracy in determining surface recombination dominance.
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.