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Growth mechanism of electroplated copper columns has been systematically studied by simulations and characterizations. A two-dimensional cross-sectional kinetic Monte Carlo (2DCS-KMC) model has been developed to simulate the electrodeposition of single crystal copper. The evolution of the microstructure has been visualized. The cluster density, average cluster size, variance of the cluster size and average aspect ratio were obtained from the simulations. The growth history of the deposition from the first atom to an equivalent of 100 monolayers was reconstructed. Following the single-lattice 2DCS-KMC model for a single crystal, a two-dimensional cross-sectional poly-lattice kinetic Monte Carlo (2DCSP-KMC) model has been developed for simulation of the electrodeposition of polycrystalline copper on both a copper and a gold substrate. With this model, the early-stage nucleation and the grain growth after impingement of nuclei can be simulated; as such the entire growth history is reconstructed in terms of the evolution of microstructure, grain statistics and grain boundary misorientation. The model is capable of capturing some key aspects of nucleation and growth mechanisms including the nucleation type (e.g. homogeneous or heterogeneous), texture development, the growth of grains and higher energetic state of grain boundaries. The model has also proven capable of capturing the effects of deposition parameters including applied electrode potential, concentration of cupric ions and temperature. Their effects are largely dependent on the substrates. The early-stage electrocrystallization of Cu on polycrystalline Au has been studied by ex-situ AFM observations. The evolution of surface morphology of the electrodeposited copper on a sputtered Au seed layer from 16ms to 1000s was observed and their formation mechanism discussed. The heterogeneous nucleation phenomenon, the competitive growth both longitudinally and laterally, and the dominant growth of some nuclei were experimentally observed, which are also visualized by the relevant KMC simulation results at a smaller size scale and a shorter time scale. A heuristic model is therefore proposed to describe the mechanism of the early-stage electrocrystallization of Cu on a polycrystalline Au seed layer. Electroplated copper columns plated for different times have been characterized in terms of the evolution of their external morphology, cross-sectional microstructure and crystal structure. The microstructure of electroplated copper columns is characteristic of bi-modal or tri-modal grain size distribution. The results indicate that recrystallization has occurred during or after the plating, top-down and laterally. Slight changes of the crystal structure were observed by in-situ XRD and it was found that the changes of the (111) and (200) planes occurred at different stages of self-annealing. Finally, the results indicate the presence of organic additives is not essential for self-annealing of a copper column to occur.

The formation of Ge nanowires in V-grooves has been studied experimentally as well as theoretically. As substrate oxide covered Si V-grooves were used formed by anisotropic etching of (001)Si wafers and subsequent oxidation of their surface. Implantation of 1E17 Ge+ cm^-2 at 70 keV was carried out into the oxide layer covering the V-grooves. Ion irradiation induces shape changes of the V-grooves, which are captured in a novel continuum model of surface evolution. It describes theoretically the effects of sputtering, redeposition of sputtered atoms, and swelling. Thereby, the time evolution of the target surface is determined by a nonlinear integro-differential equation, which was solved numerically for the V-groove geometry. A very good agreement is achieved for the predicted surface shape and the shape observed in XTEM images. Surprisingly, the model predicts material (Si, O, Ge) transport into the V-groove bottom which also suggests an Ge accumulation there proven by STEM-EDX investigations. In this Ge rich bottom region, subsequent annealing in N2 atmosphere results in the formation of a nanowire by coalescence of Ge precipitates shown by XTEM images. The process of phase separation during the nanowire growth was studied by means of kinetic 3D lattice Monte-Carlo simulations. These simulations also indicate the disintegration of continuous wires into droplets mediated by thermal fluctuations. Energy considerations have identified a fragmentation threshold and a lower boundary for the droplet radii which were confirmed by the Monte Carlo simulation. The here given results indicate the possibility of achieving nanowires being several nanometers wide by further growth optimizations as well as chains of equally spaced clusters with nearly uniform diameter.

The formation of Ge nanowires in V-grooves has been studied experimentally as well as theoretically. As substrate oxide covered Si V-grooves were used formed by anisotropic etching of (001)Si wafers and subsequent oxidation of their surface. Implantation of 1E17 Ge+ cm^-2 at 70 keV was carried out into the oxide layer covering the V-grooves. Ion irradiation induces shape changes of the V-grooves, which are captured in a novel continuum model of surface evolution. It describes theoretically the effects of sputtering, redeposition of sputtered atoms, and swelling. Thereby, the time evolution of the target surface is determined by a nonlinear integro-differential equation, which was solved numerically for the V-groove geometry. A very good agreement is achieved for the predicted surface shape and the shape observed in XTEM images. Surprisingly, the model predicts material (Si, O, Ge) transport into the V-groove bottom which also suggests an Ge accumulation there proven by STEM-EDX investigations. In this Ge rich bottom region, subsequent annealing in N2 atmosphere results in the formation of a nanowire by coalescence of Ge precipitates shown by XTEM images. The process of phase separation during the nanowire growth was studied by means of kinetic 3D lattice Monte-Carlo simulations. These simulations also indicate the disintegration of continuous wires into droplets mediated by thermal fluctuations. Energy considerations have identified a fragmentation threshold and a lower boundary for the droplet radii which were confirmed by the Monte Carlo simulation. The here given results indicate the possibility of achieving nanowires being several nanometers wide by further growth optimizations as well as chains of equally spaced clusters with nearly uniform diameter.

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

The ageing state of the world's nuclear power infrastructure, and the need to reduce humanity s dependency on fossil fuels, requires that this electrical energy generating capacity is replaced. Economic factors, and its physical and chemical properties, make high purity iron-chromium binary alloys a strong candidate for use in the construction of the pressure vessels of the next generation of nuclear reactors. This relatively inexpensive metal retains the oxidation resistance property of so-called stainless steel alloys, and has demonstrated dimensional stability and low degradation under harsh experimental environments of temperature and radiation. In this work, we consider radiation induced interstitial damage to the atomic lattices of iron-chromium binary alloys using the atomistic modelling methods, Molecular Dynamics and Adaptive Kinetic Monte Carlo, simulating collision cascade sequences, and the migration of defects in the aftermath. Variations in chromium content does not effect the initial damage production in terms of the number of Frenkel pairs produced, but iron and chromium atoms are not evenly distributed in defect atoms with respect to the bulk concentration. In simulations conducted at low temperature, chromium is under-represented, and at high temperature, a greater proportion of interstitial atoms are chromium than in the lattice overall. The latter phenomena is most strongly pronounced in systems of low bulk chromium content. During the simulation of post-cascade defect migration, interstitials atoms are observed to form temporary clusters and vacancies align along adjacent lattice sites, with the two types of defect also migrating to annihilate by recombination. Calculating the energy spectra of cascade events corresponding to an example experimental configuration using the SRIM package, we investigated the evolution of lattice systems in which a sequence of multiple cascade events occurred, both with and without a physically representative time gap between events. These simulations gave us the opportunity to observe the behaviour of cascades in the proximity of damage remaining from previous events, such as the promotion of defect clustering when this occurs.

Ces dernières décennies, la constante évolution des besoins de stockage de données a mené à un bouleversement du paysage technologique qui s’est complètement métamorphosé et réinventé. Depuis les débuts du stockage magnétique jusqu’aux plus récents dispositifs fondés sur l’électronique dit d’état solide, la densité de bits stockés continue d’augmenter vers ce qui semble du point de vue du consommateur comme des capacités de stockage et des performances infinies. Cependant, derrière chaque transition et évolution des technologies de stockage se cachent des limitations en termes de densité et performances qui nécessitent de lourds travaux de recherche afin d’être surmontées et repoussées. Ce manuscrit s’articule autour d’une technologie émergeante prometteuse ayant pour vocation de révolutionner le paysage du stockage de données : la mémoire à pont conducteur ou Conductive Bridge Random Access Memory (CBRAM). Cette technologie est fondée sur la formation et dissolution réversible d’un chemin électriquement conducteur dans un électrolyte solide. Elle offre de nombreux avantages face aux technologies actuelles tels qu’une faible consommation électrique, de très bonnes performances d’écriture et de lecture et la capacité d’être intégré aux seins des interconnexions métalliques d’une puce afin d’augmenter la densité de stockage. Malgré tout, pour que cette technologie soit compétitive certaines limitations ont besoin d’être surmontées et particulièrement sa variabilité et sa stabilité thermique qui posent encore problème. Ce manuscrit propose une compréhension physique globale du fonctionnement de la technologie CBRAM fondée sur une étude expérimentale approfondie couplée à un modèle Monte Carlo cinétique spécialement développé. Cette compréhension fait le lien entre les propriétés physiques des matériaux composant la mémoire CBRAM et ses performances (Tension et temps d’écriture et d’effacement, rétention de donnée, endurance et variabilité). Un fort accent est mis la compréhension des limites actuelle de la technologie et comment les repousser. Grâce à une optimisation des conditions d’opérations ainsi qu’à un travail d’ingénierie des dispositifs mémoire, il est démontré dans ce manuscrit une forte amélioration de la stabilité thermique ainsi que de la variabilité des états écrits et effacés. / The constant evolution of the data storage needs over the last decades have led the technological landscape to completely change and reinvent itself. From the early stage of magnetic storage to the most recent solid state devices, the bit density keeps increasing toward what seems from a consumer point of view infinite storage capacity and performances. However, behind each storage technology transition stand density and performances limitations that required strong research work to overcome. This manuscript revolves around one of the promising emerging technology aiming to revolutionize data storage landscape: the Conductive Bridge Random Access Memory (CBRAM). This technology based on the reversible formation and dissolution of a conductive path in a solid electrolyte matrix offers great advantages in term of power consumption, performances, density and the possibility to be integrated in the back end of line. However, for this technology to be competitive some roadblocks still have to be overcome especially regarding the technology variability, reliability and thermal stability. This manuscript proposes a comprehensive understanding of the CBRAM operations based on experimental results and a specially developed Kinetic Monte Carlo model. This understanding creates bridges between the physical properties of the materials involved in the devices and the devices performances (Forming, SET and RESET time and voltage, retention, endurance, variability). A strong emphasis is placed on the current limitations of the technology previously stated and how to overcome these limitations. Improvement of the thermal stability and device reliability are demonstrated with optimized operating conditions and proper devices engineering.

<div>Accelerating the development of novel materials is one of the central goals of the Materials Genome Initiative and improving the predictive power of computational</div><div>material science methods is critical to attain this goal. Molecular dynamics (MD) is one such computational technique that has been used to study a wide range of materials since its invention in the 1950s. In this work we explore some examples of using and increasing the predictive power of MD simulations to understand materials phenomena and provide guidelines to design tailored materials. We first demonstrate the use of MD simulations as a tool to explore the design space of shape memory alloys, using simple interatomic models to identify characteristics of an integrated coherent second phase that will modify the transformation characteristics of the base shape memory alloy to our desire. Our approach provides guidelines to identify potential coherent phases that will achieve tailored transformation temperatures and hysteresis. </div><div><br></div><div>We subsequently explore ideas to enhance the length and time scales accessible via MD simulations. We first discuss the use of kinetic Monte Carlo methods in MD simulations to predict the microstructure evolution of carbon fibers. We ?find our approach to accurately predict the transverse microstructures of carbon fibers, additionally predicting the transverse modulus of these fibers, a quantity difficult to measure via experiments. Another avenue to increase length and time scales accessible via MD simulations is to explore novel implementations of algorithms involved in machine-learned interatomic models to extract performance portability. Our approach here results in significant speedups and an efficient utilization of increasingly common CPU-GPU hybrid architectures.</div><div><br></div><div>We finally explore the use of machine learning methods in molecular dynamics, specifically developing machine learning methods to discover interpretable laws directly from data. As examples, we demonstrate the discovery of integration schemes for MD simulations, and the discovery of melting laws for perovskites and single elements. Overall, this work attempts to illustrate how improving the predictive capabilities of molecular dynamics simulations and incorporating machine learning ideas can help us design novel materials, in line with the goals of the Materials Genome Initiative.</div>

Metal-Oxide-Silicon Field-Effect-Transistors with a layer of electrically isolated Si nanocrystals (NCs) embedded in the gate oxide are known to improve conventional floating gate flash memories. Data retention, program and erase speeds as well as the memory operation voltages can be substantially improved due to the discrete charge storage in the isolated Si NCs. Using ion beam synthesis, Si NCs can be fabricated along with standard CMOS processing. The optimization of the location and size of ion beam synthesized Si NCs requires a deeper understanding of the mechanisms involved, which determine (i) the built-up of Si supersaturation by high-fluence ion implantation and (ii) NC formation by phase separation. For that aim, process simulations have been conducted that address both aspects on a fundamental level and, on the other hand, are able to avoid tedious experiments. The built-up of a Si supersaturation by high-fluence ion implantation were studied using dynamic binary collision calculations with TRIDYN and have lead to a prediction of Si excess depth profiles in thin gate oxides of a remarkable quality. These simulations include in a natural manner high fluence implantation effects as target erosion by sputtering, target swelling and ion beam mixing. The second stage of ion beam synthesis is modeled with the help of a tailored kinetic Monte Carlo code that combines a detailed kinetic description of phase separation on atomic level with the required degree of abstraction that is necessary to span the timescales involved. Large ensembles of Si NCs were simulated reaching the late stages of NC formation and dissolution at simulation sizes that allowed a direct comparison with experimental studies, e.g. with electron energy loss resolved TEM investigations. These comparisons reveal a nice degree of agreement, e.g. in terms of predicted and observed precipitate morphologies for different ion fluences. However, they also point clearly onto impact of additional external influences as, e.g., the oxidation of implanted Si by absorbed humidity, which was identified with the help of these process simulations. Moreover, these simulations are utilized as a general tool to identify optimum processing regimes for a tailored Si NC formation for NC memories. It is shown that key properties for NC memories as the tunneling distance from the transistor channel to the Si NCs, the NC morphology, size and density can be adjusted accurately despite of the involved degree of self-organization. Furthermore, possible lateral electron tunneling between neighboring Si NCs is evaluated on the basis of the performed kinetic Monte Carlo simulations.

Metal-Oxide-Silicon Field-Effect-Transistors with a layer of electrically isolated Si nanocrystals (NCs) embedded in the gate oxide are known to improve conventional floating gate flash memories. Data retention, program and erase speeds as well as the memory operation voltages can be substantially improved due to the discrete charge storage in the isolated Si NCs. Using ion beam synthesis, Si NCs can be fabricated along with standard CMOS processing. The optimization of the location and size of ion beam synthesized Si NCs requires a deeper understanding of the mechanisms involved, which determine (i) the built-up of Si supersaturation by high-fluence ion implantation and (ii) NC formation by phase separation. For that aim, process simulations have been conducted that address both aspects on a fundamental level and, on the other hand, are able to avoid tedious experiments. The built-up of a Si supersaturation by high-fluence ion implantation were studied using dynamic binary collision calculations with TRIDYN and have lead to a prediction of Si excess depth profiles in thin gate oxides of a remarkable quality. These simulations include in a natural manner high fluence implantation effects as target erosion by sputtering, target swelling and ion beam mixing. The second stage of ion beam synthesis is modeled with the help of a tailored kinetic Monte Carlo code that combines a detailed kinetic description of phase separation on atomic level with the required degree of abstraction that is necessary to span the timescales involved. Large ensembles of Si NCs were simulated reaching the late stages of NC formation and dissolution at simulation sizes that allowed a direct comparison with experimental studies, e.g. with electron energy loss resolved TEM investigations. These comparisons reveal a nice degree of agreement, e.g. in terms of predicted and observed precipitate morphologies for different ion fluences. However, they also point clearly onto impact of additional external influences as, e.g., the oxidation of implanted Si by absorbed humidity, which was identified with the help of these process simulations. Moreover, these simulations are utilized as a general tool to identify optimum processing regimes for a tailored Si NC formation for NC memories. It is shown that key properties for NC memories as the tunneling distance from the transistor channel to the Si NCs, the NC morphology, size and density can be adjusted accurately despite of the involved degree of self-organization. Furthermore, possible lateral electron tunneling between neighboring Si NCs is evaluated on the basis of the performed kinetic Monte Carlo simulations.