Multi-scale Monte Carlo Simulation Research Articles

Gold nanoparticle effect on dose and DNA damage enhancement in the vicinity of gold nanoparticles

This study uses Monte Carlo simulations to examine the dose enhancement effect of gold nanoparticles (AuNPs) in radiation therapy and its effects on DNA damage. Using the GATE- 9.0 and Geant4-DNA packages, Monte Carlo simulations were used to simulate a mathematical phantom and determine the energy deposition in the vicinity of AuNP. The simulations were conducted for various photon beam energies (50, 100, 250, and 6000 keV) with and without the presence of different-size AuNPs (10, 30, 50 and 100 nm). The dose enhancement factor (DER) was evaluated using Geant4-DNA to examine the effects AuNP sizes and photon beam energies on DNA damage. A multi-scale Monte Carlo simulation was conducted to evaluate enhanced DNA damage owing to nanoparticles in the proximity of cancer cells. The Monte Carlo simulations indicated that AuNPs boost the dose delivery, resulting in enhanced energy deposition and subsequent DNA damage. The DER analysis revealed a significant increase in the dose deposition within DNA, leading to single or double-strand breaks. Geant4-DNA simulations revealed information on the dosage enhancement factor for various AuNP sizes and photon beam intensities, enabling a deeper comprehension of the underlying mechanics. The outcomes of this study emphasize the potential of AuNPs as effective radiosensitizers in radiation therapy and contribute to the growing body of research on the use of nanotechnology in enhancing cancer treatment outcomes. Further investigations and experimental validations are necessary to optimize the usage of AuNPs for improved radiation therapy.

Hybrid uncertainty propagation analysis of nonlinear systems in the frequency domain based on multi-scale random interval moment method

To quantify the impact of hybrid uncertainty (random and interval parameters) on the frequency-domain nonlinear dynamic response, the multi-scale method (MSM) and the random interval moment method (RIMM) are combined to establish a new uncertainty propagation analysis method, called the multi-scale random interval moment method (MS-RIMM). RIMM is used to describe the hybrid uncertainty, while MSM is used to determine the frequency-domain nonlinear dynamic response. The statistical characteristics (i.e., the expectation value and variance) of the amplitude-frequency response of the nonlinear system with hybrid uncertainties are derived. Furthermore, the accuracy and effectiveness of the proposed method are verified by comparing the results with those obtained using the multi-scale Monte Carlo simulation method (MS-MCSM). Overall, the results of this study can serve as a useful reference for the hybrid uncertainty propagation analysis of nonlinear systems and for predicting the frequency-domain nonlinear dynamic response.

A multiscale Monte Carlo simulation of irradiating a typical-size apple by low-energy X-rays and electron beams

Irradiation of foods and fruits by ionizing radiation effectively eliminates pathogenic microorganisms primarily present on the surface. Recently, much attention has been paid to the use of low-energy X-rays and electron beams for food irradiation. Dosimetric investigation of foods with complex shapes is experimentally challenging. In this study, a multiscale Monte Carlo simulation was performed to compare the dose distributions of low-energy X-rays and electrons in a typical-size apple as well as to calculate the DNA damages induced by secondary electrons in a bacterial cell model. Several energies up to 500 keV and tube voltages up to 200 kV were considered for incident electrons and X-rays, respectively. The results indicate that on a microscopic scale, the yields of single- and double-stranded DNA breaks induced by X-rays and electrons of different energies are nearly identical. In a macroscopic scale, a two-sided electron beam configuration plus a 90-degree rotation of the apple (or the beam) provides an appropriate dose uniformity on the apple surface up to a depth of 40 μm. In the case of low-energy X-rays, apple rotation is not required when a two-sided irradiation is applied. Generally, X-rays are superior to electrons for the surface treatment of apples due to their lower dose uniformity ratios on the surface.

Multiscale Monte Carlo simulations of gold nanoparticle dose-enhanced radiotherapy II. Cellular dose enhancement within macroscopic tumor models.

Gold NanoParticle (GNP) dose-enhanced radiation therapy (GNPT) requires consideration of physics across macro- to microscopic length scales, however, this presents computational challenges that have limited previousinvestigations. To develop and apply multiscale Monte Carlo (MC) simulations to assess variations in nucleus and cytoplasm dose enhancement factors (n,cDEFs) over tumor-scalevolumes. The intrinsic variation of n,cDEFs (due to fluctuations in local gold concentration and cell/nucleus size variation) are estimated via MC modeling of varied cellular GNP uptake and cell/nucleus sizes. Then, the Heterogeneous MultiScale (HetMS) model is implemented in MC simulations by combining detailed models of populations of cells containing GNPs within simplified macroscopic tissue models to evaluate n,cDEFs. Simulations of tumors with spatially uniform gold concentrations (5, 10, or 20mgAu /gtissue ) and spatially varying gold concentrations eluted from a point are performed to determine n,cDEFs as a function of distance from the source for 10 to 370 keV photons. All simulations are performed for three different intracellular GNP configurations: GNPs distributed on the surface of the nucleus (perinuclear) and GNPs packed into one or four endosome(s). Intrinsic variations in n,cDEFs can be substantial, for example, if GNP uptake and cell/nucleus radii are varied by 20%, variations of up to 52% in nDEF and 25% in cDEF are observed compared to the nominal values for uniform cell/nucleus size and GNP concentration. In HetMS models of macroscopic tumors, subunity n,cDEFs (i.e., dose decreases) can occur for low energies and high gold concentrations due to attenuation of primary photons through the gold-filled volumes, for example, n,cDEF<1 is observed 3mm from a 20 keV source for the four endosome configuration. In HetMS simulations of tumors with spatially uniform gold concentrations, n,cDEFs decrease with depth into the tumor as photons are attenuated, with relative differences between GNP models remaining approximately constant with depth in the tumor. Similar initial n,cDEF decreases with radius are seen in the tumors with spatially varying gold concentrations, but the n,cDEFs for all of the GNP configurations converge to a single value for each energy as gold concentration reacheszero. The HetMS framework has been implemented for multiscale MC simulations of GNPT to compute n,cDEFs over tumor-scale volumes, with results demonstrating that cellular doses are highly sensitive to cell/nucleus size, GNP intracellular distribution, gold concentration, and cell position in tumor. This work demonstrates the importance of proper choice of computational model when simulating GNPT scenarios and the need to account for intrinsic variations in n,cDEFs due to variations in cell/nucleus size and goldconcentration.

Application of High-Z Gold Nanoparticles in Targeted Cancer Radiotherapy-Pharmacokinetic Modeling, Monte Carlo Simulation and Radiobiological Effect Modeling.

Simple SummaryHigh-Z gold nanoparticles show potential as radiosensitizers in the radiotherapy of cancer. In this paper, we introduce the benefits and procedures for the application of gold nanoparticles in targeted cancer radiotherapy. Based on microscopic images of the distribution of antibody-conjugated nanoparticles, we established pharmacokinetic models simulating the biodistribution of nanoparticle conjugates in the tumor and tumor environment in preclinical models. This information has been implemented in radiation transport Monte Carlo simulation codes for further investigating physical and chemical enhancement and radiobiological effects, such as DNA strand breaks and cell survival. Future perspectives and challenges of translating this promising gold nanoparticle-aided radiotherapy into clinical practice are also discussed.High-Z gold nanoparticles (AuNPs) conjugated to a targeting antibody can help to improve tumor control in radiotherapy while simultaneously minimizing radiotoxicity to adjacent healthy tissue. This paper summarizes the main findings of a joint research program which applied AuNP-conjugates in preclinical modeling of radiotherapy at the Klinikum rechts der Isar, Technical University of Munich and Helmholtz Zentrum München. A pharmacokinetic model of superparamagnetic iron oxide nanoparticles was developed in preparation for a model simulating the uptake and distribution of AuNPs in mice. Multi-scale Monte Carlo simulations were performed on a single AuNP and multiple AuNPs in tumor cells at cellular and molecular levels to determine enhancements in the radiation dose and generation of chemical radicals in close proximity to AuNPs. A biologically based mathematical model was developed to predict the biological response of AuNPs in radiation enhancement. Although simulations of a single AuNP demonstrated a clear dose enhancement, simulations relating to the generation of chemical radicals and the induction of DNA strand breaks induced by multiple AuNPs showed only a minor dose enhancement. The differences in the simulated enhancements at molecular and cellular levels indicate that further investigations are necessary to better understand the impact of the physical, chemical, and biological parameters in preclinical experimental settings prior to a translation of these AuNPs models into targeted cancer radiotherapy.

Multi-scale Monte Carlo simulations of gold nanoparticle-induced DNA damages for kilovoltage X-ray irradiation in a xenograft mouse model using TOPAS-nBio

BackgroundGold nanoparticles (AuNPs) are considered as promising agents to increase the radiosensitivity of tumor cells. However, the biological mechanisms of radiation enhancement effects of AuNPs are still not well understood. We present a multi-scale Monte Carlo simulation framework within TOPAS-nBio to investigate the increase of DNA damage due to the presence of AuNPs in mouse tumor models.MethodsA tumor was placed inside a voxel mouse model and irradiated with either 100-kVp or 200-kVp X-ray beams. Phase spaces were employed to transfer particles from the macroscopic (voxel) scale to the microscopic scale, which consists of a cell geometry including a detailed mouse DNA model. Radiosensitizing effects were calculated in the presence and absence of hybrid nanoparticles with a text{Fe}_2text{O}_3 core surrounded by a gold layer (AuFeNPs). To simulate DNA damage even for very small energy tracks, Geant4-DNA physics and chemistry models were used on microscopic scale.ResultsAn AuFeNP-induced enhancement of both dose and DNA strand breaks has been established for different scenarios. Produced chemical radicals including hydroxyl molecules, which were assumed to be responsible for DNA damage through chemical reactions, were found to be significantly increased. We further observed a dependency of the results on the location of the cells within the tumor for 200-kVp X-ray beams.ConclusionOur multi-scale approach allows to study irradiation-induced physical and chemical effects on cells. We showed a potential increase in cell radiosensitization caused by relatively small concentrations of AuFeNPs. Our new methodology allows the individual adjustment of parameters in each simulation step and therefore can be used for other studies investigating the radiosensitizing effects of AuFeNPs or AuNPs in living cells.

Understanding the role of energetic particles during the growth of TiO2 thin films by reactive magnetron sputtering through multi-scale Monte Carlo simulations and experimental deposition

In this paper, a previously established 3D multi-scale simulation chain of plasma deposition process, based on a combination of a direct simulation Monte Carlo (gas phase) algorithm and a kinetic Monte Carlo (kMC) (film growth) code, is improved by the addition of a particle-in-cell Monte Carlo collision algorithm in order to take into account and clarify the role of charged particles. The kinetic Monte Carlo code is also extended with a binary collision approximation algorithm to handle charged particles. This modelling strategy is successfully applied to the growth of TiO2 thin films by means of reactive magnetron sputtering. In order to highlight the effects of negative oxygen ions, two substrate locations are selected: one in the median plane of the targets and another one off the median plane. The model efficiently predicts the densities and fluxes of both charged and neutral particles towards the substrate. Typical results such as particle densities, the discharge current density and ion flux onto the target, and the various substrate locations are calculated. The angular distribution and energy distribution of all involved particles are sampled at these very same substrate locations and the nanoscale modelling (NASCAM) code, implementing the kMC approach, uses these results to explain the morphology of the experimentally deposited coatings. The changes throughout the transition from metallic deposition to stoichiometric TiO2 of the columnar structure of the deposited films is explained by the suppression of the atom diffusion on the growing film due to Ti oxidation. Moreover, the high-energy negative atomic oxygen ions originating from the targets are identified as the origin of the abnormally low inclination of the columnar structure experimentally observed for the oxide mode coatings. Measurements of the normalized energy flux (energy per deposited atom) are experimentally investigated to support and highlight the important role of energetic particles during film growth.

Characterizing SRAM and FF soft error rates with measurement and simulation

Soft error originating from cosmic ray is a serious concern for reliability demanding applications, such as autonomous driving, supercomputer, and public transportation system. Also, as the number of electronic devices increases, consumer electronics may require higher reliability than ever. On the other hand, device miniaturization and lower voltage operation degrade the immunity of SRAM and flip-flops in VLSI, and then soft error countermeasures will be demanded in more and more products. This paper characterizes and discusses soft error rates of SRAM and flip-flops in the terrestrial environment. Measured soft error rates of bulk and FDSOI SRAMs are presented. Also, the multiplicity and mechanism of multiple cell upsets (MCUs), which can spoil error correction code (ECC), are discussed. Flip-flops are also sensitive to radiation, but its protection is not well established since ECC cannot be applied. This paper reviews redundant FFs that are developed aiming at higher radiation immunity and demonstrates the resilience improvement with irradiation test results. Also, the importance of layout design is pointed out with a comparative study. Simulation, on the other hand, is a key technology to understand the soft error mechanism and guide radiation aware design for higher reliability. This paper outlines a physics-based multi-scale Monte Carlo simulation framework tailored for soft error simulation. The simulation flow and the model construction are explained, and some important implications are derived from the simulation results that assume 65-nm to 25-nm SRAMs. Finally, this article touches on future trends regarding device structure and overlooked secondary cosmic rays. The advantage and unique features of FinFET recently reported in literature are introduced. Also, muon-induced soft error is discussed focusing on the difference between positive and negative muons.

Investigating energy deposition in glandular tissues for mammography using multiscale Monte Carlo simulations.

To investigate energy deposition in glandular tissues of the breast on macro- and microscopic length scales in the context of mammography. Multiscale mammography models of breasts are developed, which include segmented, voxelized macroscopic tissue structure as well as nine regions of interest (ROIs) embedded throughout the breast tissue containing explicitly-modelled cells. Using a 30kVp Mo/Mo spectrum, Monte Carlo (MC) techniques are used to calculate dose to ∼mm voxels containing glandular and/or adipose tissues, as well as energy deposition on cellular length scales. ROIs consist of at least 1000 mammary epithelial cells and ∼200 adipocytes; specific energy (energy imparted per unit mass; stochastic analogue of the absorbed dose) is calculated within mammary epithelial cell nuclei. Macroscopic dose distributions within segmented breast tissue demonstrate considerable variation in energy deposition depending on depth and tissue structure. Doses to voxels containing glandular tissue vary between ∼0.1 and ∼4 times the mean glandular dose (MGD, averaged over the entire breast). Considering microscopic length scales, mean specific energies for mammary epithelial cell nuclei are ∼30% higher than the corresponding glandular voxel dose. Additionally, due to the stochastic nature of radiation, there is considerable variation in energy deposition throughout a cell population within a ROI: for a typical glandular voxel dose of 4mGy, the standard deviation of the specific energy for mammary epithelial cell nuclei is 85% relative to the mean. Thus, for a glandular voxel dose of 4mGy at the centre of the breast, corresponding mammary epithelial cell nuclei will receive specific energies up to ∼9mGy (considering the upper end of the 1σ standard deviation of the specific energy), while a ROI located 2cm closer to the radiation source will receive specific energies up to ∼40mGy. Energy deposition within mammary epithelial cell nuclei is sensitive to cell model details including cellular elemental compositions and nucleus size, underlining the importance of realistic cellular models. There is considerable variation in energy deposition on both macro- and microscopic length scales for mammography, with glandular voxel doses and corresponding cell nuclei specific energies many times higher than the MGD in parts of the breast. These results should be considered for radiation-induced cancer risk evaluation in mammography which has traditionally focused on a single metric such as the MGD.

An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: a feasibility study

A novel and versatile “bottom-up” approach is developed to estimate the radiobiological effect of clinic radiotherapy. The model consists of multi-scale Monte Carlo simulations from organ to cell levels. At cellular level, accumulated damages are computed using a spectrum-based accumulation algorithm and predefined cellular damage database. The damage repair mechanism is modeled by an expanded reaction-rate two-lesion kinetic model, which were calibrated through replicating a radiobiological experiment. Multi-scale modeling is then performed on a lung cancer patient under conventional fractionated irradiation. The cell killing effects of two representative voxels (isocenter and peripheral voxel of the tumor) are computed and compared. At microscopic level, the nucleus dose and damage yields vary among all nucleuses within the voxels. Slightly larger percentage of cDSB yield is observed for the peripheral voxel (55.0%) compared to the isocenter one (52.5%). For isocenter voxel, survival fraction increase monotonically at reduced oxygen environment. Under an extreme anoxic condition (0.001%), survival fraction is calculated to be 80% and the hypoxia reduction factor reaches a maximum value of 2.24. In conclusion, with biological-related variations, the proposed multi-scale approach is more versatile than the existing approaches for evaluating personalized radiobiological effects in radiotherapy.

X-ray diffraction from nonuniformly stretched helical molecules.

The fibrous proteins in living cells are exposed to mechanical forces interacting with other subcellular structures. X-ray fiber diffraction is often used to assess deformation and movement of these proteins, but the analysis has been limited to the theory for fibrous molecular systems that exhibit helical symmetry. However, this approach cannot adequately interpret X-ray data from fibrous protein assemblies where the local strain varies along the fiber length owing to interactions of its molecular constituents with their binding partners. To resolve this problem a theoretical formulism has been developed for predicting the diffraction from individual helical molecular structures nonuniformly strained along their lengths. This represents a critical first step towards modeling complex dynamical systems consisting of multiple helical structures using spatially explicit, multi-scale Monte Carlo simulations where predictions are compared with experimental data in a 'forward' process to iteratively generate ever more realistic models. Here the effects of nonuniform strains and the helix length on the resulting magnitude and phase of diffraction patterns are quantitatively assessed. Examples of the predicted diffraction patterns of nonuniformly deformed double-stranded DNA and actin filaments in contracting muscle are presented to demonstrate the feasibly of this theoretical approach.

A method to perform multi-scale Monte Carlo simulations in the clinical setting.

In order to model the track structure of clinical mega-voltage photon beams in a reasonable time, it is necessary to use a multi-scale approach incorporating a track-structure algorithm for the regions of interest and a condensed history algorithm for the rest of the geometry. This paper introduces a multi-scale Monte Carlo system, which is used to hand off particle trajectory information between the two algorithms. Since condensed history algorithms ignore electrons with energy below a fixed threshold and those electrons are important to the track structure on the micrometre scale, it is necessary to hand over all charged particles to the track-structure algorithm only in a volume that extends beyond the scoring volume. Additionally, the system is validated against experimental results for radio-isotope gamma spectra.

A multi-scale Monte Carlo method for electrolytes

Artifacts arise in the simulations of electrolytes using periodic boundary conditions (PBCs). We show the origin of these artifacts are the periodic image charges and the constraint of charge neutrality inside the simulation box, both of which are unphysical from the view point of real systems. To cure these problems, we introduce a multi-scale Monte Carlo (MC) method, where ions inside a spherical cavity are simulated explicitly, while ions outside are treated implicitly using a continuum theory. Using the method of Debye charging, we explicitly derive the effective interactions between ions inside the cavity, arising due to the fluctuations of ions outside. We find that these effective interactions consist of two types: (1) a constant cavity potential due to the asymmetry of the electrolyte, and (2) a reaction potential that depends on the positions of all ions inside. Combining the grand canonical Monte Carlo (GCMC) with a recently developed fast algorithm based on image charge method, we perform a multi-scale MC simulation of symmetric electrolytes, and compare it with other simulation methods, including PBC + GCMC method, as well as large scale MC simulation. We demonstrate that our multi-scale MC method is capable of capturing the correct physics of a large system using a small scale simulation.

Impact of Nuclear Reaction Models on Neutron-Induced Soft Error Rate Analysis

Terrestrial neutron-induced soft error rate (SER) analyses in the 25-nm design rule MOSFET are performed by means of multiscale Monte Carlo simulation with different nuclear reaction models used in PHITS code. It is clarified that a choice of nuclear reaction models has a great effect on the SER prediction. Even if the calculated production cross sections for secondary ions are the same among different reaction models, the difference in double-differential cross sections results in essential impact on the calculated SERs. Through validation of the nuclear reaction models used in PHITS code, it is concluded that the combined use of the e-mode with JENDL-4.0 below 20 MeV and the MQMD plus GEM above 20 MeV is the most suitable for the soft error simulation.

Nuclear Reaction Models Responsible for Simulation of Neutron-induced Soft Errors in Microelectronics

Terrestrial neutron-induced soft errors in MOSFETs from a 65 nm down to a 25 nm design rule are analyzed by means of multi-scale Monte Carlo simulation using the PHITS-HyENEXSS code system. Nuclear reaction models implemented in PHITS code are validated by comparisons with experimental data. From the analysis of calculated soft error rates, it is clarified that secondary He and H ions provide a major impact on soft errors with decreasing critical charge. It is also found that the high energy component from 10 MeV up to several hundreds of MeV in secondary cosmic-ray neutrons has the most significant source of soft errors regardless of design rule.

A High-Performance Cellular Automaton Model of Tumor Growth with Dynamically Growing Domains.

Tumor growth from a single transformed cancer cell up to a clinically apparent mass spans many spatial and temporal orders of magnitude. Implementation of cellular automata simulations of such tumor growth can be straightforward but computing performance often counterbalances simplicity. Computationally convenient simulation times can be achieved by choosing appropriate data structures, memory and cell handling as well as domain setup. We propose a cellular automaton model of tumor growth with a domain that expands dynamically as the tumor population increases. We discuss memory access, data structures and implementation techniques that yield high-performance multi-scale Monte Carlo simulations of tumor growth. We discuss tumor properties that favor the proposed high-performance design and present simulation results of the tumor growth model. We estimate to which parameters the model is the most sensitive, and show that tumor volume depends on a number of parameters in a non-monotonic manner.

Multi-Scale Monte Carlo Simulation of Soft Errors Using PHITS-HyENEXSS Code System

We have proposed a multi-scale Monte Carlo simulation method of neutron induced soft errors by linking a particle transport code PHITS and a 3-D TCAD simulator HyENEXSS. An interface tool between PHITS and HyENEXSS is developed to generate the mesh structure optimized for an event where multiple secondary ions extending to arbitrary directions are generated simultaneously by neutron incidence on device. Using the interface tool, we have made it possible to perform the Monte Carlo calculation of soft error rates (SERs) based on event-by-event device simulation. The PHITS-HyENEXSS code system has been successfully applied to SER analyses for 65 nm, 45 nm, and 32 nm technology MOSFETs.

Multiscale prediction of patient-specific platelet function under flow

AbstractDuring thrombotic or hemostatic episodes, platelets bind collagen and release ADP and thromboxane A2, recruiting additional platelets to a growing deposit that distorts the flow field. Prediction of clotting function under hemodynamic conditions for a patient's platelet phenotype remains a challenge. A platelet signaling phenotype was obtained for 3 healthy donors using pairwise agonist scanning, in which calcium dye–loaded platelets were exposed to pairwise combinations of ADP, U46619, and convulxin to activate the P2Y1/P2Y12, TP, and GPVI receptors, respectively, with and without the prostacyclin receptor agonist iloprost. A neural network model was trained on each donor's pairwise agonist scanning experiment and then embedded into a multiscale Monte Carlo simulation of donor-specific platelet deposition under flow. The simulations were compared directly with microfluidic experiments of whole blood flowing over collagen at 200 and 1000/s wall shear rate. The simulations predicted the ranked order of drug sensitivity for indomethacin, aspirin, MRS-2179 (a P2Y1 inhibitor), and iloprost. Consistent with measurement and simulation, one donor displayed larger clots and another presented with indomethacin resistance (revealing a novel heterozygote TP-V241G mutation). In silico representations of a subject's platelet phenotype allowed prediction of blood function under flow, essential for identifying patient-specific risks, drug responses, and novel genotypes.

Atomic Ordering in Nano-layered FePt: Multiscale Monte Carlo Simulation

AbstractCombined nano- and mesoscale simulation of chemical ordering kinetics in nano-layered L10 AB binary system was performed. In the nano- (atomistic) scale Monte Carlo (MC) technique with vacancy mechanism of atomic migration was implemented with diverse system models. The mesoscale microstructure evolution was, in turn, modeled by means of MC procedure simulating antiphase boundary (APB) motion as controlled by APB energies evaluated within the nano-scale simulations. The study addressed FePt thin layers considered as a material for ultra-high density magnetic storage media and revealed metastability of the L10 c-variant superstructure with monoatomic planes parallel to the (001) free surface and off-plane easy magnetization. The layers, initially perfectly ordered in the L10 c-variant, showed homogenous disordering running in parallel with a spontaneous re-orientation of the monoatomic planes into a mosaic-microstructure composed of L10 a- and b-variant domains with (100)- and (010)-type monoatomic planes, respectively. The domains nucleated heterogeneously on the Fe free surface of the layer, grew discontinuously inwards its volume and finally relaxed generating an equilibrium microstructure of the system. Two �atomistic-scale� processes: (i) homogenous disordering and (ii) nucleation of the L10 a- and b-variant domains showed characteristic time scales. The same was observed for the meso-scale processes: (i) heterogeneous L10 variant domain growth and (ii) domain microstructure relaxation. The above phenomena modelled within the present study by means of multiscale MC simulations have recently been observed experimentally in epitaxially deposited thin films of FePt.

Multiscale Monte Carlo Simulation of Circular DC Magnetron Sputtering: Influence of Magnetron Design on Target Erosion and Film Deposition

We present an experimental and computational study of the effect of magnetron design on the target erosion profile, wafer-level deposition uniformity, and feature-scale deposition topography in a circular DC magnetron sputtering system. We have conducted a multiscale Monte Carlo simulation consisting of a particle-in-cell Monte Carlo collision plasma simulation for target sputter erosion, a collisional transport simulation for film deposition, and a feature-scale deposition topography simulation. The entire simulation results for each step are verified in comparison with the experimental results for various process conditions. A new semiempirical calibration method, for converting an estimated ion current density distribution into a target erosion profile, is proposed with regard to the gas rarefaction effect due to sputtering wind. In conclusion, the whole performance indices of a circular DC magnetron sputtering system are substantially influenced by magnetron design, and the effectiveness of our multiscale Monte Carlo simulation methodology is validated.