Dataset Information

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 consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H₂ plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications.

SUBMITTER: Dogan G

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

REPOSITORIES: biostudies-literature

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

Dogan Gül G Demir Sinan O SO Gutzler Rico R Gruhn Herbert H Dayan Cem B CB Sanli Umut T UT Silber Christian C Culha Utku U Sitti Metin M Schütz Gisela G Grévent Corinne C Keskinbora Kahraman K

ACS applied materials & interfaces 20211104 45

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 address ...[more]

PMID: 34735111

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Dataset Information

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

Publications

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

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