Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability.

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Publication Details

Journal
Journal of Materials Chemistry C
Published
2025-11-10
DOI
https://doi.org/10.1039/d5tc03190g
Citations
1
Primary Topic
Thin-Film Transistor Technologies
Type
article
Field-Weighted Citation Impact
0.73
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article

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

1 citations
Journal of Materials Chemistry C
Thin-Film Transistor Technologies
0.73
article

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

article en
1 citations

Abstract

Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability.

Journal of Materials Chemistry CVol. 14(1)
Korea Institute for Advanced Study (KR), Seoul National University (KR), Incheon National University (KR), Samsung (South Korea) (KR)
Reduced inequalities
Openalex Percentile: Top 21%
Thin-Film Transistor Technologies
0.73
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