This thesis addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO^2 surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and ≈1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multi-dimensional Boolean valued functions in three dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. The third topic is the formation of ripple structures on ion bombarded semiconductor surfaces treated in the first topic as the prepatterned substrate of the metallic deposition. This intriguing phenomenon has been known since the 1960\'s and various theoretical approaches have been explored. These previous models are discussed and a new non-linear model is formulated, based on the local atomic flow and associated density change in the near surface region. Within this framework ripple structures are shown to form without the necessity to invoke surface diffusion or large sputtering as important mechanisms. The model can also be extended to the case where sputtering is important and it is shown that in this case, certain \\lq magic\' angles can occur at which the ripple patterns are most clearly defined. The results including some analytic solutions of the nonlinear equation of motions are in very good agreement with experimental observation.

Single-crystalline nanostructures often exhibit gradients of surface (and/or interface) curvature that emerge from fabrication and growth processes or from thermal fluctuations. Thus, the system-inherent capillary force can initiate morphological transformations during further processing steps or during operation at elevated temperature. Therefore and because of the ongoing miniaturization of functional structures which causes a general rise in surface-to-volume ratios, solid-state capillary phenomena will become increasingly important: On the one hand diffusion-mediated capillary processes can be of practical use in view of non-conventional nanostructure fabrication methods based on self-organization mechanisms, on the other hand they can destroy the integrity of nanostructures which can go along with the failure of functionality. Additionally, capillarity-induced shape transformations are effected and can thereby be controlled by applied fields and forces (guided or driven evolution). With these prospects and challenges at hand, formation and shape transformation of single-crystalline nanostructures due to the system-inherent capillary force in combination with external fields or forces are investigated in the frame of this dissertation by means of atomistic computer simulations. For the exploration (search, description, and prediction) of reaction pathways of nanostructure shape transformations, kinetic Monte Carlo (KMC) simulations are the method of choice. Since the employed KMC code is founded on a cellular automaton principle, the spatio-temporal development of lattice-based N-particle systems (N up to several million) can be followed for time spans of several orders of magnitude, while considering local phenomena due to atomic-scale effects like diffusion, nucleation, dissociation, or ballistic displacements. In this work, the main emphasis is put on nanostructures which have a cylindrical geometry, for example, nanowires (NWs), nanorods, nanotubes etc.

Defect-driven transport of impurities plays a key role in the microstructure evolution of alloys, and has a great impact on the mechanical properties at the macroscopic scale. This phenomenon is greatly enhanced in irradiated materials because of the large amount of radiation-induced crystal defects (vacancies and interstitials). For instance, the formation of nanosized solute clusters in neutron-irradiated reactor pressure vessel (RPV) ferritic steels has been shown to hinder dislocation motion and induce hardening and embrittlement. In Swedish RPV steels, this mechanical-property degradation is enhanced by the high content of manganese and nickel impurities. It has been suggested that the formation of Mn-Ni-rich clusters (which contain also Cu, Si, and P) might be the outcome of a dynamic process, where crystal defects act both as nucleation sites and solute carriers. Solute transport by point defects is therefore a crucial mechanism to understand the origin and the dynamics of the clustering process. The first part of this work aims at modeling solute transport by point defects in dilute iron alloys, to identify the intrinsic diffusion mechanisms for a wide range of impurities. Transport and diffusion coefficients are obtained by combining accurate ab initio calculations of defect transition rates with an exact mean-field model. The results show that solute drag by single vacancies is a common phenomenon occurring at RPV temperature (about 300 °C) for all impurities found in the solute clusters, and that transport of phosphorus and manganese atoms is dominated by interstitial-type defects. These transport tendencies confirm that point defects can indeed carry impurities towards nucleated solute clusters. Moreover, the obtained flux-coupling tendencies can also explain the observed radiation-induced solute enrichment on grain boundaries and dislocations. In the second part of this work, the acquired knowledge about solute-transport mechanisms is transferred to kinetic Monte Carlo (KMC) models, with the aim of simulating the RPV microstructure evolution. Firstly, the needed parameters in terms of solute-defect cluster stability and mobility are calculated by means of dedicated KMC simulations. Secondly, an innovative approach to the prediction of transition rates in complex multicomponent alloys is introduced. This approach relies on a neural network based on ab initio-computed migration barriers. Finally, the evolution of the Swedish RPV steels is simulated in a "gray-alloy" fashion, where impurities are introduced indirectly as a modification of the defect-cluster mobilities. The latter simulations are compared to the experimental characterization of the Swedish RPV surveillance samples, and confirm the possibility that solute clusters might form on small interstitial clusters. In conclusion, this work identifies from a solid theoretical perspective the atomic-transport phenomena underlying the formation of embrittling nanofeatures in RPV steels. In addition, it prepares the ground for the development of predictive KMC tools that can simulate the microstructure evolution of a wide variety of irradiated alloys. This is of great interest not only for reactor pressure vessels, but also for many other materials in extreme environments. / <p>QC 20151123</p>

This thesis is presented in two sections. Two different multiscale models are developed in order to increase the computational speed of two well known atomistic algorithms, Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC). In Section I, the MD method is introduced. Following this, a multiscale method of linking an MD simulation of heat conduction to a finite element (FE) simulation is presented. The method is simple to implement into a conventional MD code and is independent of the atomistic model employed. This bridge between the FE and MD simulations works by ensuring that energy is conserved across the FE/MD boundary. The multiscale simulation allows for the investigation of large systems which are beyond the range of MD. The method is tested extensively in the steady state and transient regimes, and is shown to agree with well with large scale MD and FE simulations. Furthermore, the method removes the artificial boundary effects due to the thermostats and hence allows exact temperatures and temperature gradients to be imposed on to an MD simulation. This allows for better study of temperature gradients on crystal defects etc. In Section II, the KMC method is introduced. A continuum model for the KMC method is presented and compared to the standard KMC model of surface diffusion. This method replaces the many discrete back and forth atom jumps performed by a standard KMC algorithm with a single flux that can evolve in time. Elastic strain is then incorporated into both algorithms and used to simulate atom deposition upon a substrate by Molecular Beam Epitaxy. Quantum dot formation due to a mismatch in the lattice spacing between a substrate and a deposited film is readily observed in both models. Furthermore, by depositing alternating layers of substrate and deposit, self-organised quantum dot super-lattices are observed in both models.

One of the most important challenges in low-temperature fuel cell technology is improving the catalytic efficiency at the electrode-catalyst where the oxygen reduction reaction (ORR) occurs. Platinum is the best pure catalyst for this reaction but its high cost and scarcity hinder the commercial implementation of fuel cells in automobiles. Pt-based alloys are promising alternatives to substitute platinum while maintaining the efficiency and life-time of the pure catalyst. However, the acid medium and the oxidation of the surface reduce the activity and durability of the alloy catalyst through changes in its local composition and structure. Molecular simulation techniques are applied to characterize the thermodynamics and dynamic evolution of the surface of platinum-based alloy catalysts under reaction conditions.1-10 A simulation scheme of the surface oxidation is proposed which combines classical molecular dynamics (MD) and density functional theory (DFT). This approach is able to reproduce the main features of the oxidation phenomena observed experimentally, it is concluded that the dissolution mechanism of metal atoms involves: 1) Surface segregation of alloy atoms, 2) oxygen absorption into the subsurface of the catalyst, and 3) metal detachment through the interaction with ions in the solvent. Therefore, to improve the durability of platinum-based alloy catalysts, the steps of the dissolution mechanism must be prevented. A versatile 3-D kinetic Monte Carlo (KMC) code is developed to study the degradation and dealloying in nanocatalysts. The results on the degradation of Pt nanoparticles under different potential regimes demonstrate that the dissolution depends on the potential path to which the nanocatalyst is exposed. Metal atoms detach from the boundaries of (111) facets expecting a reduction in the activity of the nanoparticle. Also, the formation of Pt hollow nanoparticles by the Kirkendall effect is addressed, the role of vacancies is crucial in the removal of the non-noble core that yields to hollow nanoparticles. To investigate the reasons for the experimentally found enhanced ORR activity in porous/hollow nanoparticles, the effect of subsurface vacancies on the main ORR activity descriptors is studied with DFT. It is found that an optimum amount of vacancies may enhance the ORR activity of Pt-monolayer catalysts over certain alloy cores by changing the binding energies of O and OH.

La réduction des dimensions des dispositifs CMOS (Complementary Metal Oxide Semiconductor) implique de nombreux défis dans la formation de jonctions. La recroissance par épitaxie en phase solide (SPER) à des températures inférieures à 600 °C est une technique attrayante dans la mesure où elle permet de réaliser des jonctions abruptes avec une forte concentration de dopants actifs et qui sont nécessaires pour les nœuds avancés tels que le 20 nm et au-delà. Dans ce manuscrit, on présente un modèle atomistique basé sur la méthode Monte-Carlo cinétique sur réseau (LKMC) afin de simuler la cinétique de SPER dans le silicium. Le modèle s'appuie sur la description phénoménologique des mécanismes microscopiques de recristallisation proposé par Drosd et Washburn dans [J. Appl. Phys. 53, 397 (1982)] en distinguant des événements {100}, {110} et {111} selon le plan local de recroissance et a été implémenté dans le simulateur MMonCa [Appl. Phys. Lett. 98, 233109 (2011)]. Il s'agit de la même base que le modèle de Martín-Bragado et Moroz [Appl. Phys. Lett. 95, 123123 (2009)] qui a été implémenté dans le simulateur commercial Synopsys SProcess KMC. Néanmoins, dans notre travail, la formation de macles lors des évènements {111} a été introduite ce qui a nécessité des changements importants dans l'implémentation. Le modèle a été calibré sur des résultats expérimentaux et permet de prédire l'anisotropie et la dépendance en température. En particulier, il a été utilisé afin d'expliquer la formation de zones défectueuses dans les dispositifs FDSOI à l'issue de la SPER à une température réduite. Le modèle LKMC a, en outre, été étendu dans le but d'inclure l'influence d'une contrainte non-hydrostatique et la recroissance accélérée du fait de la présence de dopants actifs. Les effets d'une contrainte non-hydrostatique ont été introduits en utilisant le concept de tenseur d'activation proposé par Aziz, Sabin et Lu dans [Phys. Rev. B 44, 9812 (1991)] et seulement quatre paramètres indépendants sont nécessaires. La présence de dopants ionisés cause une accélération de la vitesse de recroissance qui est attribué à un effet lié à la position du niveau de Fermi à l'interface amorphe/cristal. Un solveur 3D auto-cohérent de l'équation de Poisson avec le modèle de Thomas-Fermi a été implémenté et couplé avec le modèle LKMC afin de prendre en compte la courbure des bandes à l'interface amorphe/cristal. La correction phénoménologique de décalage du niveau de Fermi généralisé (GFLS) proposée par Williams et Elliman dans [Phys. Rev. Lett. 51, 1069 (1983)] a été utilisée pour modifier les fréquences de recristallisation des évènements microscopiques. Des simulations de la vitesse de recroissance en fonction de la température pour différentes concentrations de dopants ont montré un bon accord avec les données expérimentales. En résumé, dans ce manuscrit, un modèle unifié de SPER basé sur une approche LKMC est présentée. Il prend en compte l'influence de différents paramètres sur la cinétique de recroissance et ayant un intérêt technologique tels que la température, l'orientation cristalline, la contrainte et la présence de dopants. Le modèle est, en soi, tridimensionnel et permet donc d'explorer les phénomènes de recroissance impliquant plusieurs fronts de recristallisation et qui ont lieu lors du procédé de fabrication de dispositifs électroniques réels. / Complementary metal oxide semiconductor (CMOS) device scaling involves many technologicalchallenges in terms of junction formation. Solid phase epitaxial regrowth (SPER) at temperaturesbelow 600 ˝C is an attractive technique since it enables to form highly–activated andabrupt junctions that are required for advanced technology nodes such as 20 nm and beyond.In this manuscript, we present a comprehensive atomistic model relying on the lattice KineticMonte Carlo (LKMC) method to simulate SPER kinetics in silicon. The model is based onthe phenomenological description of the microscopic recrystallization mechanisms proposedby Drosd and Washburn in [J. Appl. Phys. 53, 397 (1982)] by distinguishing among {100},{110} and {111} events depending on the local regrowth plane and has been implemented inthe MMonCa simulator [Appl. Phys. Lett. 98, 233109 (2011)]. This is the same basis than theatomistic model of Martín–Bragado and Moroz proposed in [Appl. Phys. Lett. 95, 123123(2009)] and available in the Synopsys SProcess KMC commercial tool. Nevertheless, in ourwork the formation of twin configurations during {111} events has been incorporated givingrise to significant changes in the implementation. The model has been calibrated on single–directional SPER experiments and allows predicting the regrowth anisotropy and temperaturedependence. In particular, it has been used to explain the formation of defective regions inFDSOI devices annealed with a low processing temperature. In this work, the LKMC modelhas also been extended in order to include the influence of non–hystrostatic stress and dopant–enhanced regrowth that are technologically relevant. Non–hydrostatic stress effects have beenincorporated using the concept of activation strain tensor introduced by Aziz, Sabin and Luin [Phys. Rev. B 44, 9812 (1991)] and only four independent parameters are required. Thepresence of ionized dopants has been shown to cause an enhancement of the regrowth velocitywhich has been attributed to a Fermi level effect. A three–dimensional Thomas–Fermi–Poisson solver has been implemented and coupled with the LKMC model allowing to takeinto account the band bending at amorphous/crystalline interface. The phenomenological generalizedFermi level shifting (GFLS) correction proposed by Williams and Elliman in [Phys.Rev. Lett. 51, 1069 (1983)] has been used to modify the microscopic recrystallization rates.Simulations of the regrowth velocity as a function of temperature for different dopant concentrationshave shown a reasonable agreement with experimental data. In summary, in thismanuscript a unified SPER model relying on the LKMC approach is presented. It takes intoaccount various technologically relevant parameters influencing the regrowth kinetics such astemperature, crystalline orientation, stress and dopants. The model is per se three-dimensionaland can therefore be used to explore multi–directional regrowth phenomena that take place inreal electronic devices.

In recent years, there has been increased awareness that stochasticity in chemical reactions and diffusion of molecules can have significant effects on the outcomes of intracellular processes, particularly given the low copy numbers of many proteins and mRNAs present in a cell. For such molecular species, the number and locations of molecules can provide a more accurate and detailed description than local concentration. In addition to diffusion, drift in the movements of molecules can play a key role in the dynamics of intracellular processes, and can often be modeled as arising from potential fields. Examples of sources of drift include active transport, variations in chemical potential, material heterogeneities in the cytoplasm, and local interactions with subcellular structures. This dissertation presents a new numerical method for simulating the stochastically varying numbers and locations of molecular species undergoing chemical reactions and drift-diffusion. The method combines elements of the First-Passage Kinetic Monte Carlo (FPKMC) method for reaction-diffusion systems and the Wang—Peskin—Elston lattice discretization of the Fokker—Planck equation that describes drift-diffusion processes in which the drift arises from potential fields. In the FPKMC method, each molecule is enclosed within a "protective domain," either by itself or with a small number of other molecules. To sample when a molecule leaves its protective domain or a reaction occurs, the original FPKMC method relies on analytic solutions of one- and two-body diffusion equations within the protective domains, and therefore cannot be used in situations with non-constant drift. To allow for such drift in our new method (hereafter Dynamic Lattice FPKMC or DL-FPKMC), each molecule undergoes a continuous-time random walk on a lattice within its protective domain, and the lattices change adaptively over time. One of the most commonly used spatial models for stochastic reaction-diffusion systems is the Smoluchowski diffusion-limited reaction (SDLR) model. The DL-FPKMC method generates convergent realizations of an extension of the SDLR model that includes drift from potentials. We present detailed numerical results demonstrating the convergence and accuracy of our method for various types of potentials (smooth, discontinuous, and constant). We also present several illustrative applications of DL-FPKMC, including examples motivated by cell biology.

abstract: Programmable metallization cell (PMC) technology employs the mechanisms of metal ion transport in solid electrolytes (SE) and electrochemical redox reactions in order to form metallic electrodeposits. When a positive bias is applied to an anode opposite to a cathode, atoms at the anode are oxidized to ions and dissolve into the SE. Under the influence of the electric field, the ions move to the cathode and become reduced to form the electrodeposits. These electrodeposits are filamentary in nature and persistent, and since they are metallic can alter the physical characteristics of the material on which they are formed. PMCs can be used as next generation memories, radio frequency (RF) switches and physical unclonable functions (PUFs). The morphology of the filaments is impacted by the biasing conditions. Under a relatively high applied electric field, they form as dendritic elements with a low fractal dimension (FD), whereas a low electric field leads to high FD features. Ion depletion effects in the SE due to low ion diffusivity/mobility also influences the morphology by limiting the ion supply into the growing electrodeposit. Ion transport in SE is due to hopping transitions driven by drift and diffusion force. A physical model of ion hopping with Brownian motion has been proposed, in which the ion transitions are random when time window is larger than characteristic time. The random growth process of filaments in PMC adds entropy to the electrodeposition, which leads to random features in the dendritic patterns. Such patterns has extremely high information capacity due to the fractal nature of the electrodeposits. In this project, lateral-growth PMCs were fabricated, whose LRS resistance is less than 10Ω, which can be used as RF switches. Also, an array of radial-growth PMCs was fabricated, on which multiple dendrites, all with different shapes, could be grown simultaneously. Those patterns can be used as secure keys in PUFs and authentication can be performed by optical scanning. A kinetic Monte Carlo (KMC) model is developed to simulate the ion transportation in SE under electric field. The simulation results matched experimental data well that validated the ion hopping model. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

In calculating the time evolution of an atomic system on diffusive timescales, off-lattice kinetic Monte Carlo (OLKMC) can sometimes be used to overcome the limitations of Molecular Dynamics. OLKMC relies on the harmonic approximation to Transition State Theory, in which the rate of rare transitions from one energy minimum to a neighboring minimum scales exponentially with an energy barrier on the potential energy surface. This requires locating the index-1 saddle point, commonly referred to as a transition state, that separates two neighboring energy minima. In modeling the evolution of an atomic system, it is desirable to find all the relevant transitions surrounding the current minimum. Due to the large number of minima on the potential energy surface, exhaustively searching the landscape for these saddle points is a challenging task. In examining the particular case of isolated Lennard-Jones clusters of around 50 particles, we observe very slow convergence of the total number of saddle points found as a function of successful searches. We seek to understand this behavior by modeling the distribution of successful searches and sampling this distribution to create a stochastic process that mimics this behavior. Finally, we will discuss an improvement to a rejection scheme for OLKMC where we terminate searches that appear to be failing early in the search process.

<p>Laser cutting is a manufacturing technology that uses a focused laser beam to melt,burn and vaporize materials, resulting in a high-quality cut edge. Although previous efforts are primarily based on a trial-and-error approach, there is insufficient understanding of the laser cutting process, thus hindering further development of the technology. Therefore, the motivation of this thesis is to address this research need by developing a series of models tounderstand the thermal and microstructure evolution in the process.</p> <p>The goal of the thesis is to design a tool for optimizing the steel laser cutting processthrough a modeling approach. The goal will be achieved through three interrelated objec-tives: (1) understand the thermal field in the laser cutting process of ASTM A36 steel using the finite element (FE) method coupled with the user-defined Moving Heat Source package;(2) apply machine learning method to predict heat-affected zone (HAZ) and kerf, the keyfeatures in the laser cutting process; and (3) employ kinetic Monte Carlo (kMC) simulationto simulate the resultant microstructures in the laser cutting process.</p> <p>Specifically, in the finite element model, a laser beam was applied to the model with the parameters of the laser’s power, cut speed, and focal diameter being tested. After receiving results generated by the finite element model, they were then used by two machine learning algorithms to predict the HAZ distance and kerf width that is produced due to the laser cutting process. The two machine learning algorithms tested were a neural network and asupport vector machine. Finally, the thermal field was imported into the kMC model as the boundary conditions to predict grain evolution’s in the metals.</p> <p>The results of the research showed that by increasing the focal diameter of a laser, the kerf width can be decreased and the HAZ distance experienced a large decrease. Additionally, apulse-like pattern was observed in the kerf width through modeling and can be minimized into more of a uniform cut through the increase of the focal diameter. By increasing thepower of a laser, the HAZ distance, kerf width, and region of the material above its original temperature increase. Additionally, through the increase of the cut speed, the HAZ distance, kerf width, kerf pulse-like pattern, and region of the material above its original temperature decrease.</p> <p>Through the incorporation of machine learning algorithms, it was found that they can be used to effectively predict the HAZ distance to a certain degree. The Neural Networkand Support Vector Machine models both show that the experimental HAZ distance datalines up with the results derived from ANSYS. The Gaussian Process Regression HAZ model shows that the algorithm is not powerful enough to create an accurate prediction. Additionally, all of the kerf width models show that the experimental data is being overfit by the ANSYS results. As such, the kerf width results from ANSYS need additional validation to prove their accuracy.</p> <p>Using the kMC model to examine the microstructure change due to the laser cutting process, three observations were made. First, the largest grain growth occurs at the edge ofthe laser where the material was not hot enough to be cut. Then, grain growth decays as thedistance from the edge increases. Finally, at the edge of the HAZ boundary, grain growth does not occur.</p>