Projects per year
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.
Original language | English |
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Article number | 157253 |
Number of pages | 9 |
Journal | Applied Surface Science |
Volume | 626 |
Early online date | 15 Apr 2023 |
DOIs | |
Publication status | Published - 30 Jul 2023 |
Keywords
- nanolithography
- ReaxFF MD
- bias-induced oxidation
- chemical composition
- silicon
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Towards determinstic atomic scale manufacturing of next-generation quantum devices
EPSRC (Engineering and Physical Sciences Research Council)
1/01/23 → 31/12/24
Project: Research Fellowship
Datasets
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Data for: "Atomistic insights into bias-induced oxidation on passivated silicon surface through ReaxFF MD simulation"
Gao, J. (Creator) & Luo, X. (Supervisor), University of Strathclyde, 19 Apr 2023
DOI: 10.15129/773fb2bc-7a7e-49de-96e5-04b55251f2db
Dataset