Simulation in thin film technology

Turowski, Marcus; Jupé, Marco; Ehlers, Henrik; Melzig, Thomas; Pflug, Andreas; Ristau, Detlev

doi:10.1117/12.2191693

Cited by 11 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Badorreck et al [6] investigated GLAD coatings of TiO 2 with regard to the birefringence properties. However, the former performed simulations with deposition under 0 • , 30 • , 50 • and 70 • exhibit a small size in lateral dimension of 7 × 8 nm 2 ; therefore, here the results will be compared to structures grown on a substrate area of 30 × 20 nm 2 . In addition, the impact of using different interaction potentials will be analyzed here, namely the potentials from Matsui et al [9] and Zhang et al [10].…”

Section: Methodsmentioning

confidence: 99%

“…However, the computation of the deposition of thin films is a challenge, because for a concise model several orders of magnitude in space and time have to be covered by a multi-scale model. As described in [1][2][3], this involves several models from direct simulation Monte Carlo (DSMC), to atomistic models like molecular dynamics (MD) or kinetic Monte Carlo (kMC), to quantum mechanical density functional theory (DFT) calculations. Within the framework of the theoretical description, the complete virtualization of the coating process place enormous demands on both the computational technology and the numerical procedures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Analysis on the Structural Size of Simulated Thin Film Growth with Molecular Dynamics for Glancing Angle Incidence Deposition

et al. 2021

Self Cite

View full text Add to dashboard Cite

For the purpose of a deeper understanding of thin film growth, in the last two decades several groups developed models for simulation on the atomistic scale. Models using molecular dynamics as their simulation method already give results comparable to experiments, however statistical analysis of the simulations themselves are lacking so far, reasoned by the limits imposed by the computational power and parallelization that can only be used in lateral dimensions. With advancements of software and hardware, an increase in simulation speed by a factor of up to 10 can be reached. This allows either larger structures and/or more throughput of the simulations. The paper analyses the significance of increasing the structure size in lateral dimensions and also the repetition of simulations to gain more insights into the statistical fluctuation contained in the simulations and how well the coincidence with the experiment is. For that, glancing angle incidence deposition (GLAD) coatings are taken as an example. The results give important insights regarding the used interaction potential, the structure size and resulting important differences for the density, surface morphology, roughness and anisotropy. While larger structures naturally can reproduce the real world in more detail, the results show which structure sizes are needed for these aspects without wasting computational resources.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Analysis on the Structural Size of Simulated Thin Film Growth with Molecular Dynamics for Glancing Angle Incidence Deposition

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The next study conducted by the same author focused on the simulation of thin-film technology for TiO 2 [ 24 ]. The paper introduced an extension to the virtual quoted concept, wherein the determined optical properties are linked with rare acquisition models.…”

Section: Modeling Techniquesmentioning

confidence: 99%

Modeling of Magnetic Films: A Scientific Perspective

Misiurev,

Holcman

2024

Materials

View full text Add to dashboard Cite

Magnetic thin-film modeling stands as a dynamic nexus of scientific inquiry and technological advancement, poised at the vanguard of materials science exploration. Leveraging a diverse suite of computational methodologies, including Monte Carlo simulations and molecular dynamics, researchers meticulously dissect the intricate interplay governing magnetism and thin-film growth across heterogeneous substrates. Recent strides, notably in multiscale modeling and machine learning paradigms, have engendered a paradigm shift in predictive capabilities, facilitating a nuanced understanding of thin-film dynamics spanning disparate spatiotemporal regimes. This interdisciplinary synergy, complemented by avantgarde experimental modalities such as in situ microscopy, promises a tapestry of transformative advancements in magnetic materials with far-reaching implications across multifaceted domains including magnetic data storage, spintronics, and magnetic sensing technologies. The confluence of computational modeling and experimental validation heralds a new era of scientific rigor, affording unparalleled insights into the real-time dynamics of magnetic films and bolstering the fidelity of predictive models. As researchers chart an ambitiously uncharted trajectory, the burgeoning realm of magnetic thin-film modeling burgeons with promise, poised to unlock novel paradigms in materials science and engineering. Through this intricate nexus of theoretical elucidation and empirical validation, magnetic thin-film modeling heralds a future replete with innovation, catalyzing a renaissance in technological possibilities across diverse industrial landscapes.

show abstract

“…8 At the same time, computational methods have been developed, enabling researchers to handle hundreds of molecules and, hence, to model organic films. 14…”

Section: Introductionmentioning

confidence: 99%