Design of a film surface roughness-minimizing molecular beam epitaxy

Molecular beam epitaxy of germanium was used along with kinetic Monte Carlo simulations to study time-varying processing parameters and their effect on surface morphology. Epitaxial Ge films were deposited on highly oriented Ge(001) substrates, with reflection high-energy electron diffraction as a real-time sensor. The Monte Carlo simulations were used to model the growth process, and physical parameters were determined during growth under time-varying flux. A reduced version of the simulations was generated, enabling the application on an optimization algorithm. Temperature profiles were then computed that minimize surface roughness subject to various experimental constraints. The final roughness after two layers of growth was reduced to 0.32, compared to 0.36 at the maximum growth temperature. The study presented here is an initial demonstration of a general approach that could also be used to optimize properties in other materials and deposition processes

Effective transition rates for epitaxial growth using fast modulation

Thin-film deposition is an industrially important process that is highly dependent on the processing conditions. Most films are grown under constant conditions, but a few studies show that modified properties may be obtained with periodic inputs. However, assessing the effects of modulation experimentally becomes impractical with increasing material complexity. Here we consider periodic conditions in which the period is short relative to the time scales of growth. We analyze a stochastic model of thin-film growth, computing effective transition rates associated with rapid periodic process parameters. Combinations of effective rates may exist that are not attainable under steady conditions, potentially enabling new film properties. An algorithm is presented to construct the periodic input for a desired set of effective transition rates. These ideas are demonstrated in three simple examples using kinetic Monte Carlo simulations of epitaxial growth

IMECE2005-81153 MODELING AND CHARACTERIZATION OF DIELECTROPHORETIC ASSEMBLY PROCESS FOR NANOBELTS

ABSTRACT Robust manufacturing methods are needed for nanocomponent assembly, and one must understand the physics to optimize the processing and to develop control schemes to deal with the inherent uncertainty. We are studying field induced assembly of a new class of semiconducting metal oxides -nanobelts -that have been demonstrated for chemical sensing. We have demonstrated the integration of nanobelts with electrodes to make sensors by dielectrophoresis (DEP). The SnO 2 nanobelts (width ~ 100 -300 nm, thickness ~ 30 -40 nm) were suspended in ethanol and introduced into a microchannel, and were assembled across the electrodes. Modeling suggests that attraction should occur at all frequencies over this range. Targeted experiments were performed to quantify surface and material properties for input to the modeling, and FEMLAB simulations were performed to validate the model. The goal of the modeling is to optimize the assembly of nanostructures in a manufacturing process at the wafer-scale. INTRODUCTION A fascinating range of new materials with previously unattainable properties are being developed by nanoscientists. Applications of these new materials include nanowire-based electronics, nanosensors, optical systems, flat panel displays that use carbon nanotubes, high heat flux modified surfaces and biological and biomedical applications. However, the assembly of these nanostructured materials into nanometer-scale device

Reduction and Identification Methods for Nonhomogeneous Markovian Control Systems, with Applications to Thin Film Deposition

Abstract Dynamic models of nanometer-scale phenomena often require an explicit consideration of interactions among a large number of atoms or molecules. The corresponding mathematical representation may thus be high-dimensional and stochastic, incompatible with tools in nonlinear control theory that are designed for low-dimensional deterministic equations. We consider here a general class of probabilistic systems that are linear in the state, but whose input enters as a function multiplying the state vector. Model reduction is accomplished by grouping probabilities that evolve together, and truncating states that are unlikely to be accessed. An error bound for this reduction is also derived. A system identification approach that exploits the inherent linearity is then developed, that generates all coefficients in either a full or reduced model. These concepts are then extended to extremely high-dimensional systems, in which kinetic Monte Carlo simulations provide the input-output data. This work was motivated by our interest in thin film deposition. We demonstrate the approaches developed in the paper on a kinetic Monte Carlo simulation of surface evolution during film growth, and use the reduced model to compute optimal temperature profiles that minimize surface roughness