<ul data-eligibleForWebStory="true"><li>Researchers are investigating surrogate models to enhance plasma assisted atomic layer deposition (PEALD) in high aspect ratio features.</li><li>Plasma-based processes like PEALD can face challenges from surface recombination, requiring long exposure times for full conformality in high aspect ratio vias.</li><li>Artificial neural networks were trained on a synthetic dataset generated from PEALD simulations to predict saturation times based on cross section thickness data from partially coated conditions.</li><li>Results show that just two experiments in undersaturated conditions provide enough information to predict saturation times accurately within 10% of the actual time.</li><li>A surrogate model achieved 99% accuracy in determining whether surface recombination dominates plasma-surface interactions in PEALD processes.</li><li>Machine learning offers a faster route for optimizing PEALD processes in applications such as microelectronics.</li><li>The approach can also be extended to atomic layer etching and more complex structures.</li></ul>

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

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