Atomistic insights into bias-induced oxidation on passivated silicon surface through ReaxFF MD simulation

Gao, Jian and Luo, Xichun and Xie, Wenkun and Qin, Yi and Hasan, Rashed Md. Murad and Fan, Pengfei (2023) Atomistic insights into bias-induced oxidation on passivated silicon surface through ReaxFF MD simulation. Applied Surface Science, 626. 157253. ISSN 0169-4332 (https://doi.org/10.1016/j.apsusc.2023.157253)

[thumbnail of Gao-etal-ASS-2023-Atomistic-insights-into-bias-induced-oxidation-on-passivated-silicon-surface]
Preview
 Text. Filename: Gao_etal_ASS_2023_Atomistic_insights_into_bias_induced_oxidation_on_passivated_silicon_surface.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (9MB)| Preview

Abstract

The study investigated the bias-induced oxidation through ReaxFF molecular dynamics simulations in order to bridge the knowledge gaps in the understanding of physical-chemical reaction at the atomic scale. Such an understanding is critical to realise accurate process control of bias-induced local anodic oxidation nanolithography. In this work, we simulated bias-induced oxidation by applying electric fields to passivated silicon surfaces and performed a detailed analysis of the simulation results to identify the primary chemical components involved in the reaction and their respective roles. In contrast to surface passivation, bias-induced oxidation led mainly to the creation of Si–O–Si bonds in the oxide film, along with the consumption of H2O and the generation of H3O+ in the water layer, whereas the chemical composition on the oxidised surface remained essentially unchanged with a mixture of Si–O–H, Si–H, Si–H2, H2O–Si and Si–O–Si bonds. Furthermore, parametric studies indicated that increased electric field strength and humidity did not significantly alter the surface chemical composition but notably enhanced the bias-induced oxidation, as indicated by the increased number of Si–O–Si bonds and oxide thickness in simulation results. A good agreement is achieved between the simulation and experimental results.

ORCID iDs

Gao, Jian ORCID logoORCID: https://orcid.org/0000-0001-7740-5274, Luo, Xichun ORCID logoORCID: https://orcid.org/0000-0002-5024-7058, Xie, Wenkun ORCID logoORCID: https://orcid.org/0000-0002-5305-7356, Qin, Yi ORCID logoORCID: https://orcid.org/0000-0001-7103-4855, Hasan, Rashed Md. Murad and Fan, Pengfei;
CORE (COnnecting REpositories)