Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers.

ABSTRACT: Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al₂O₃ passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consis

SUBMITTER: Dogan G 

PROVIDER: S-EPMC8603353 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature