Site-controlled formation of single Si nanocrystals in a buried SiO2 matrix using ion beam mixing.

For future nanoelectronic devices - such as room-temperature single electron transistors - the site-controlled formation of single Si nanocrystals (NCs) is a crucial prerequisite. Here, we report an approach to fabricate single Si NCs via medium-energy Si+ or Ne+ ion beam mixing of Si into a buried SiO2 layer followed by thermally activated phase separation. Binary collision approximation and kinetic Monte Carlo methods are conducted to gain atomistic insight into the influence of relevant experimental parameters on the Si NC formation process. Energy-filtered transmission electron microscopy is performed to obtain quantitative values on the Si NC size and distribution in dependence of the layer stack geometry, ion fluence and thermal budget. Employing a focused Ne+ beam from a helium ion microscope, we demonstrate site-controlled self-assembly of single Si NCs. Line irradiation with a fluence of 3000 Ne+/nm2 and a line width of 4 nm leads to the formation of a chain of Si NCs, and a single NC with 2.2 nm diameter is subsequently isolated and visualized in a few nanometer thin lamella prepared by a focused ion beam (FIB). The Si NC is centered between the SiO2 layers and perpendicular to the incident Ne+ beam.

Multiscale Self-Assembly of Silicon Quantum Dots into an Anisotropic Three-Dimensional Random Network.

Multiscale self-assembly is ubiquitous in nature but its deliberate use to synthesize multifunctional three-dimensional materials remains rare, partly due to the notoriously difficult problem of controlling topology from atomic to macroscopic scales to obtain intended material properties. Here, we propose a simple, modular, noncolloidal methodology that is based on exploiting universality in stochastic growth dynamics and driving the growth process under far-from-equilibrium conditions toward a preplanned structure. As proof of principle, we demonstrate a confined-but-connected solid structure, comprising an anisotropic random network of silicon quantum-dots that hierarchically self-assembles from the atomic to the microscopic scales. First, quantum-dots form to subsequently interconnect without inflating their diameters to form a random network, and this network then grows in a preferential direction to form undulated and branching nanowire-like structures. This specific topology simultaneously achieves two scale-dependent features, which were previously thought to be mutually exclusive: good electrical conduction on the microscale and a bandgap tunable over a range of energies on the nanoscale.

Faceted nanostructure arrays with extreme regularity by self-assembly of vacancies.

Semiconductor quantum dots and wires are important building blocks for future electronic and optoelectronic devices. The common way of producing semiconductor nanostructures is by molecular beam epitaxy (MBE). In this additive growth process atoms are deposited onto crystalline surfaces and self-assemble into 3D structures. Here we present a subtractive process, in which surface vacancies are created by ion impacts. On terraces of crystalline surfaces their nucleation forms depressions which coarsen and finally lead to a self-organized 3D morphology. It is shown that this kind of spontaneous pattern formation is inherent to the ion induced erosion process on crystalline surfaces and is analogous to 3D growth by MBE. However, novel facets are found due to slightly different energetics and kinetics of ad-atoms and surface vacancies, especially at Ehrlich-Schwoebel step-edge barriers. Depending on the crystal orientation, three-fold, four-fold, six-fold symmetry, as well as extremely regular periodic nanogrooves can be produced on different orientations of group IV (Si, Ge) and III-V (GaAs, InAs) semiconductors.

MaterialVis: material visualization tool using direct volume and surface rendering techniques.

Visualization of the materials is an indispensable part of their structural analysis. We developed a visualization tool for amorphous as well as crystalline structures, called MaterialVis. Unlike the existing tools, MaterialVis represents material structures as a volume and a surface manifold, in addition to plain atomic coordinates. Both amorphous and crystalline structures exhibit topological features as well as various defects. MaterialVis provides a wide range of functionality to visualize such topological structures and crystal defects interactively. Direct volume rendering techniques are used to visualize the volumetric features of materials, such as crystal defects, which are responsible for the distinct fingerprints of a specific sample. In addition, the tool provides surface visualization to extract hidden topological features within the material. Together with the rich set of parameters and options to control the visualization, MaterialVis allows users to visualize various aspects of materials very efficiently as generated by modern analytical techniques such as the Atom Probe Tomography.

Ordered Ag nanocluster structures by vapor deposition on pre-patterned SiO2.

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. Computer modeling of the growth has been performed with 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 adatoms and ≈1 nm square surface migration ranges of Ag adatoms. It is also shown that metal nucleations can trigger even on defect free surfaces. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns.

Surface pattern formation and scaling described by conserved lattice gases.

We extend our 2+1 -dimensional discrete growth model [Odor, Phys. Rev. E 79, 021125 (2009)] with conserved, local exchange dynamics of octahedra, describing surface diffusion. A roughening process was realized by uphill diffusion and curvature dependence. By mapping the slopes onto particles, two-dimensional nonequilibrium binary lattice model emerges, in which the (smoothing or roughening) surface diffusion can be described by attracting or repelling motion of oriented dimers. The binary representation allows simulations on very large size and time scales. We provide numerical evidence for Mullins-Herring or molecular-beam epitaxy class scaling of the surface width. The competition of inverse Mullins-Herring diffusion with a smoothing deposition, which corresponds to a Kardar-Parisi-Zhang (KPZ) process, generates different patterns: dots or ripples. We analyze numerically the scaling and wavelength growth behavior in these models. In particular, we confirm by large size simulations that the KPZ type of scaling is stable against the addition of this surface diffusion, hence this is the asymptotic behavior of the Kuramoto-Sivashinsky equation as conjectured by field theory in two dimensions, but has been debated numerically. If very strong, normal surface diffusion is added to a KPZ process, we observe smooth surfaces with logarithmic growth, which can describe the mean-field behavior of the strong-coupling KPZ class. We show that ripple coarsening occurs if parallel surface currents are present, otherwise logarithmic behavior emerges.

Directed d -mer diffusion describing the Kardar-Parisi-Zhang-type surface growth.

We show that d+1-dimensional surface growth models can be mapped onto driven lattice gases of d-mers. The continuous surface growth corresponds to one dimensional drift of d-mers perpendicular to the (d-1-dimensional "plane" spanned by the d-mers. This facilitates efficient bit-coded algorithms with generalized Kawasaki dynamics of spins. Our simulations in d=2, 3, 4, 5 dimensions provide scaling exponent estimates on much larger system sizes and simulations times published so far, where the effective growth exponent exhibits an increase. We provide evidence for the agreement with field theoretical predictions of the Kardar-Parisi-Zhang universality class and numerical results. We show that the (2+1)-dimensional exponents conciliate with the values suggested by Lässig within error margin, for the largest system sizes studied here, but we cannot support his predictions for (3+1)d numerically.

Mapping of (2+1) -dimensional Kardar-Parisi-Zhang growth onto a driven lattice gas model of dimers.

We show that a (2+1) -dimensional discrete surface growth model exhibiting Kardar-Parisi-Zhang (KPZ) class scaling can be mapped onto a two-dimensional conserved lattice gas model of directed dimers. The KPZ height anisotropy in the surface model corresponds to a driven diffusive motion of the lattice gas dimers. We confirm by numerical simulations that the scaling exponents of the dimer model are in agreement with those of the (2+1) -dimensional KPZ class. This opens up the possibility of analyzing growth models via reaction-diffusion models, which allow much more efficient computer simulations.