Machine learning-based exploration of molecular design descriptors for area-selective atomic layer deposition (AS-ALD) precursors.
Area-selective deposition
Atomic layer deposition
Feature selection
Machine learning
Molecular design
Journal
Journal of molecular modeling
ISSN: 0948-5023
Titre abrégé: J Mol Model
Pays: Germany
ID NLM: 9806569
Informations de publication
Date de publication:
14 Dec 2023
14 Dec 2023
Historique:
received:
09
10
2023
accepted:
07
12
2023
medline:
14
12
2023
pubmed:
14
12
2023
entrez:
13
12
2023
Statut:
epublish
Résumé
Area-selective atomic layer deposition (AS-ALD) is a thin film deposition technique developed using conventional ALD by considering the surface chemical nature of the substrate. Selecting appropriate precursors is a critical step in developing an efficient AS-ALD process with high deposition selectivity. However, the current efficiency of research on viable AS-ALD precursors is limited because of the absence of theoretical design rules for precursor chemical structures. In this study, our objective is to propose molecular design principle for precursors for AS-ALD, particularly focusing on achieving high deposition selectivity of oxides on diverse substrates. Current preliminary results suggest that ML-based prediction model may provide a fundamental molecular-level understanding of the reactivity of metal oxide precursors, that can be useful for efficient selection of suitable precursors for AS-ALD. We employ density functional theory (DFT) calculations and machine learning (ML) techniques to analyze the relationship between the structure and the surface reactivity of the precursor. Considering DFT calculation data (M06L/def2-tzvp, Gaussian 09 and Orca 4.0) and information on precursor structures, artificial neural networks (ANN, neuralnet, R) are applied to identify critical descriptors of the AS-ALD process. Furthermore, we utilize this ANN model to predict precursor reactivity according to surface terminations.
Identifiants
pubmed: 38093140
doi: 10.1007/s00894-023-05806-y
pii: 10.1007/s00894-023-05806-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10Subventions
Organisme : National Research Foundation of Korea
ID : RS-2022-00143881
Organisme : National Research Foundation of Korea
ID : RS-2023-00210186
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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