Atomistic insights on hydrogen plasma treatment for stabilizing High-k/Si interface

Kim, Byungjo; Kim, Muyoung; Yoo, Suyoung; Nam, Sang Ki

doi:10.1016/j.apsusc.2022.153297

Cited by 3 publications

(5 citation statements)

References 74 publications

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“…ReaxFF, which was developed by van Duin et al [32,33], is a typical example of a reactive force field that was successfully implemented to understand plasma-material interactions. In our previous study, hydrogen plasma-treated surfaces were thoroughly investigated using ReaxFF for the Si/H system [34].…”

Section: Data Preparation With MD Simulationsmentioning

confidence: 99%

“…Then, temperature rescaling was applied for 100 steps of the NVT ensemble with a Berendsen thermostat to reflect internal dissipation [38,39]. After a single incidence of F atom, the products or reflected species beyond the deposition region were deleted to mimic the etching phenomena at the target surface, similarly to our previous study [34]. The single incidence event was repeated to satisfy the target fluence of 1.04 × 10 16 cm −2 (3000 F atoms).…”

Section: Initial Fluorinated Surface Modelingmentioning

confidence: 99%

“…The kinetic energy of the Ar atom was 1 keV toward the surface with a predefined incident angle from the surface normal direction. Here, the charge of the Ar ion was ignored [34,40]. During bombardment, the NVE ensemble was applied to the Newtonian atom region to capture the projectile motion of the impinging Ar atom and the resultant sputtered species.…”

Section: Ion Bombardment Simulationmentioning

confidence: 99%

“…From the stored atomic configurations, the surface slab region was defined via cluster analysis with OVITO [73] using a cutoff distance of 2.5 Å, and the detached atoms from the surface slab region were treated as sputtered atoms. Once the detached atoms were classified, the bond connectivity was examined with ReaxFF using a cutoff of bond order 0.3 [34]. The chemical species were identified and differentiated by considering the bond connectivity between the individual atoms.…”

Section: Appendix B Sputtered Product Monitoring With MD Simulationsmentioning

confidence: 99%

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Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

Kim¹,

Bae²,

Jeong³

et al. 2023

J. Phys. D: Appl. Phys.

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With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma–material interactions—particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion–surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion–surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion–surface interactions. In the ROM framework, a conditional variational autoencoder (CVAE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as autoencoder (AE), sparse AE (SAE), contractive AE (CAE), denoising AE (DAE), and variational AE (VAE). From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle > 80°), diffuse reflection behavior was observed, which can substantially affect the ion transport in the HAR patterns. Moreover, it was hypothesized that a shift in sputtering characteristics occurs as the surface F coverage varies, based on the inferred EADs. This conjecture was confirmed through detailed MD simulations that demonstrated the fundamental relationship between surface atomic conformations and their sputtering behavior. Combined with additional atomistic-scale investigations, this framework can provide an efficient way to reveal various fundamental plasma–material interactions which are highly demanded for the future development of semiconductor device manufacturing.

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Section: Data Preparation With MD Simulationsmentioning

confidence: 99%

Section: Initial Fluorinated Surface Modelingmentioning

confidence: 99%

Section: Ion Bombardment Simulationmentioning

confidence: 99%

Section: Appendix B Sputtered Product Monitoring With MD Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

Kim¹,

Bae²,

Jeong³

et al. 2023

J. Phys. D: Appl. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…), many materials, especially metals, can be treated with hydrogen plasma treatment. Indeed, it has been seen that the hydrogen species generated by hydrogen plasma treatment allows one to reach an effective surface passivation on semiconductor devices [20][21][22]. Moreover, it has been seen that the improvement obtained in the passivation layer due to the diffusion of hydrogen to the amorphous-crystalline silicon interface can also circumvent some limits regarding epitaxial growth as it occurs for defects and structural disorders [23].…”

Section: Introductionmentioning

confidence: 99%

SiO2/SiC Nanowire Surfaces as a Candidate Biomaterial for Bone Regeneration

Ghezzi,

Attolini,

Bosi

et al. 2023

Crystals

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Tissue engineering (TE) and nanomedicine require devices with hydrophilic surfaces to better interact with the biological environment. This work presents a study on the wettability of cubic silicon-carbide-based (SiC) surfaces. We developed four cubic silicon-carbide-based epitaxial layers and three nanowire (NW) substrates. Sample morphologies were analyzed, and their wettabilities were quantified before and after a hydrogen plasma treatment to remove impurities due to growth residues and enhance hydrophilicity. Moreover, sample biocompatibility has been assessed with regard to L929 cells. Our results showed that core–shell nanowires (SiO2/SiC NWs), with and without hydrogen plasma treatment, are the most suitable candidate material for biological applications due to their high wettability that is not influenced by specific treatments. Biological tests underlined the non-toxicity of the developed biomaterials with regard to murine fibroblasts, and the proliferation assay highlighted the efficacy of all the surfaces with regard to murine osteoblasts. In conclusion, SiO2/SiC NWs offer a suitable substrate to develop platforms and membranes useful for biomedical applications in tissue engineering due to their peculiar characteristics.

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Influence of Surface Halogenation on Silane Adsorption onto Si(001) Surface for Poly-Si Epitaxy: A Density Functional Theory Study with Neural Network Potential

Jeong,

Kim,

Yoo

et al. 2023

2023 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)

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Atomistic insights on hydrogen plasma treatment for stabilizing High-k/Si interface

Cited by 3 publications

References 74 publications

Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

SiO2/SiC Nanowire Surfaces as a Candidate Biomaterial for Bone Regeneration

Influence of Surface Halogenation on Silane Adsorption onto Si(001) Surface for Poly-Si Epitaxy: A Density Functional Theory Study with Neural Network Potential

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