Multiscale plasma and feature profile simulations of plasma-enhanced chemical vapor deposition and atomic layer deposition processes for titanium thin film fabrication

Denpoh, Kazuki; Moroz, Paul; Kato, T; Matsukuma, Masaaki

doi:10.7567/1347-4065/ab5bc9

Cited by 19 publications

(21 citation statements)

References 37 publications

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“…In fact, since the showerhead has many very small holes to disperse the gas mixture uniformly into the discharge volume, the showerhead has a complex structure, which should be considered in a three-dimensional geometry. However, the gas mixture fed through the showerhead was assumed to have radially uniform density and temperature distributions in the showerhead inlet, as similarly assumed in other groups' previous reports about CCP simulations [34][35][36].…”

Section: Resultsmentioning

confidence: 99%

Theoretical Analysis of Si2H6 Adsorption on Hydrogenated Silicon Surfaces for Fast Deposition Using Intermediate Pressure SiH4 Capacitively Coupled Plasma

Park

Kim

2021

Coatings

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The rapid and uniform growth of hydrogenated silicon (Si:H) films is essential for the manufacturing of future semiconductor devices; therefore, Si:H films are mainly deposited using SiH4-based plasmas. An increase in the pressure of the mixture gas has been demonstrated to increase the deposition rate in the SiH4-based plasmas. The fact that SiH4 more efficiently generates Si2H6 at higher gas pressures requires a theoretical investigation of the reactivity of Si2H6 on various surfaces. Therefore, we conducted first-principles density functional theory (DFT) calculations to understand the surface reactivity of Si2H6 on both hydrogenated (H-covered) Si(001) and Si(111) surfaces. The reactivity of Si2H6 molecules on hydrogenated Si surfaces was more energetically favorable than on clean Si surfaces. We also found that the hydrogenated Si(111) surface is the most efficient surface because the dissociation of Si2H6 on the hydrogenated Si(111) surface are thermodynamically and kinetically more favorable than those on the hydrogenated Si(001) surface. Finally, we simulated the SiH4/He capacitively coupled plasma (CCP) discharges for Si:H films deposition.

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Section: Resultsmentioning

confidence: 99%

Theoretical Analysis of Si2H6 Adsorption on Hydrogenated Silicon Surfaces for Fast Deposition Using Intermediate Pressure SiH4 Capacitively Coupled Plasma

Park

Kim

2021

Coatings

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“…Denpoh et al 86 developed a multiscale model for the Ti-PECVD and Ti-PEALD processes with TiCl 4 ∕H 2 ∕Ar plasma and studied the coverage of the deposited Ti film on complex SiO 2 ∕SiN stacked films with multiple reentries. As for gas phase calculations, code of CFD-ACE+ 87 incorporating 30 species and 87 reactions was used in the case of the CCP reactor with an RF of 450 kHz.…”

Section: Cvd and Pvd Modelsmentioning

confidence: 99%

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

Kuboi

2023

J. Micro/Nanopattern. Mats. Metro.

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.Modeling and simulation of feature scale profiles, including damage distributions and film properties in dry etching (continuous wave, pulse, cycle, and atomic layer etching) and deposition processes (chemical vapor, physical vapor, and atomic layer deposition) using string, shock tracking, level-set, and cell removable (or voxel) methods, are useful for predicting process properties and gain insights into the variation mechanisms for realizing high performance of advanced complementary metal-oxide semiconductor devices and have been comprehensively reviewed in this work. The future perspective has also been discussed from the viewpoint of a fusion physical model with machine learning using real-time monitoring of the manufacturing equipment, so-called process informatics. Furthermore, after the introduction of quantum computing for process simulations on atomic scales, the epoch of full digital twins might be realized.

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“…They used plasma information based virtual metrology for plasma etching with experimental data sources [600], as well as model predictive control for atmospheric pressure plasma dose delivery [601] or reactive magnetron sputtering close to mode transition [602]. In contrast, theoretical multi-scale analyses of technological plasmas have been restricted to classical modeling and simulation (e.g., combining molecular dynamics, binary collision approximation, and kinetic Monte Carlo models at the atomic level [603]; or unidirectional coupling the reactor scale to the feature scale in complex capacitive radio frequency plasmas [604]; list not exhaustive).…”

Section: Data Management In Manufacturingmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Multiscale plasma and feature profile simulations of plasma-enhanced chemical vapor deposition and atomic layer deposition processes for titanium thin film fabrication

Cited by 19 publications

References 37 publications

Theoretical Analysis of Si2H6 Adsorption on Hydrogenated Silicon Surfaces for Fast Deposition Using Intermediate Pressure SiH4 Capacitively Coupled Plasma

Theoretical Analysis of Si2H6 Adsorption on Hydrogenated Silicon Surfaces for Fast Deposition Using Intermediate Pressure SiH4 Capacitively Coupled Plasma

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

2022 Review of Data-Driven Plasma Science

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