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Machine learning-based exploration of molecular design descriptors for area-selective atomic layer deposition (AS-ALD) precursors

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Abstract

Context

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.

Methods

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.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was funded by the Ministry of Trade, Industry and Energy (Grant Number: RS-2022–00143881). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, RS-2023–00210186).

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Author notes

  1. Tran Thi Ngoc Van and Changsu Kim contributed equally to this work.

Authors and Affiliations

  1. Chemical Engineering, Hongik University, Seoul, 04066, South Korea

    Tran Thi Ngoc Van, Hojae Lee & Bonggeun Shong

  2. Chemical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea

    Changsu Kim & Jiyong Kim

Authors
  1. Tran Thi Ngoc Van
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  2. Changsu Kim
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  3. Hojae Lee
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Contributions

Conceptualization: JK, BS. Data curation: TTNV, CK. Formal Analysis: TTNV, CK. Funding acquisition: JK, BS. Investigation: TTNV, CK. Methodology: TTNV, CK. Project administration: JK, BS. Resources: HL, JK, BS. Software: TTNV, CK. Supervision: JK, BS. Validation: TTNV, CK, HL. Visualization: TTNV, CK. Writing – original draft: TTNV, CK. Writing – review & editing: HL, JK, BS.

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Correspondence to Jiyong Kim or Bonggeun Shong.

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Ngoc Van, T.T., Kim, C., Lee, H. et al. Machine learning-based exploration of molecular design descriptors for area-selective atomic layer deposition (AS-ALD) precursors. J Mol Model 30, 10 (2024). https://doi.org/10.1007/s00894-023-05806-y

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