Model-Based Control with Stochastic Simulators: Building Process Design and Control Software for Advanced Materials Processing Technology

An analysis was made on the financial feasibility of a start-up company that will sell software developed for the off-line optimization and on-line control of a thin film deposition process. This analysis found some niche applications for a potential startup company that sells thin film deposition modeling and control software solutions. Due to the potential versatility of the software that was developed, other potential markets may exist. This investigation found that the startup company can be competitive over a five year time horizon with a 20% IRR. Molecular modeling software that employs the kinetic Monte Carlo method was used for the simulation of thin film growth. Due to the capability of this model to retain both surface and internal atomic structure of the thin film, this model can simulate thin film properties such as roughness and porosity. Development work was done on producing a suitable objective function to represent a set of application-imposed thin film micro-structure property requirements. This objective function was used in the generation of an optimal transient profile. A model predictive control framework was designed to control film growth based on the objective function and the optimal transient evolution of the film. The model predictive control algorithm was analyzed and shown to perform the desired control

Design and Control Using Stochastic Models of Deposition Reactors

The financial feasibility of the creation of a start-up company to sell software developed for the optimization and in-line control of thin film growth in deposition processes was investigated. An analysis of the current marketplace revealed potential for a small start-up company to be competitive with this novel product. The investigation concluded an IRR of 20% for a five year period before possible sale of the company. The kinetic Monte Carlo method was employed as the basis for all simulations in this work. This method retains atomic scale information while enabling simulation of process relevant features such as roughness, growth rate and efficiency. A model predictive controller was designed to reproducibly generate thin films with desired properties under a variety of initial condition disturbances for both single component and multi component systems. The substrate temperature and gas flux were employed as control variables. The control algorithms were investigated using a sensitivity analysis and shown to be robust under a wide range of conditions

Growth of and Compositional Inhomogeneities in III-V-Bi Alloys

Solubility dynamicsin materials science drives discovery and novel material development. Bi has garnered interest in III-V semiconductors because of its impact in the electronic properties including largebandgap reduction per percentage Bi, high spin orbit coupling, and preserved electron mobility. The endpoints of III-Bi binary compounds are either unstable,likeGaBi, or havelow melting point, likeInBi. Finding good conditions in which there is appreciable Bi incorporation is difficult. Growths often result in droplets on the surface. This dissertation explores the phenomena surrounding incorporation of Bi including development of computational tools for investigation of Bi impact on electronic structure, a new incorporation model taking Bi surface buildup into account, an investigation into the inhomogeneities that appear along with Ga droplets, and the inhomogeneities that appears in growths with a clean surface. A new kinetic model is constructed to account for Bi buildup on the surface building on previous models. Results of experiments in GaSbBi are utilized to confirm trends predictedin the model. Bi droplets are found to reduce Bi incorporation by becoming a kinetic sink. A series of growths in GaAsBi with varying As:Ga ratio shows that Ga droplets contributes to Bi inhomogeneity in the bulk characterized by x-ray diffraction, transmission electron microscopy, and atom probe tomography. The mechanism associated with this phenomenonis non-uniform Ga availability at the growth surface due to droplet wicking. Growths with clean surfaces are also shown to exhibit inhomogeneities including Bi clusters, lateral composition modulation, and nanopores at growth temperatures below 325°C. These phenomena areexplained by a destabilization in the growth mode due to differences in surface diffusivity of As and Bi. Some preliminary data is presented for future directions including GaAsBi/GaAs superlattices, growth interrupts, and mapping the surface morphology in experiments.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145803/1/rtait_1.pd

Nanoscale thermal transport

Rapid progress in the synthesis and processing of materials with structure on nanometer length scales has created a demand for greater scientific understanding of thermal transport in nanoscale devices, individual nanostructures, and nanostructured materials. This review emphasizes developments in experiment, theory, and computation that have occurred in the past ten years and summarizes the present status of the field. Interfaces between materials become increasingly important on small length scales. The thermal conductance of many solid–solid interfaces have been studied experimentally but the range of observed interface properties is much smaller than predicted by simple theory. Classical molecular dynamics simulations are emerging as a powerful tool for calculations of thermal conductance and phonon scattering, and may provide for a lively interplay of experiment and theory in the near term. Fundamental issues remain concerning the correct definitions of temperature in nonequilibrium nanoscale systems. Modern Si microelectronics are now firmly in the nanoscale regime—experiments have demonstrated that the close proximity of interfaces and the extremely small volume of heat dissipation strongly modifies thermal transport, thereby aggravating problems of thermal management. Microelectronic devices are too large to yield to atomic-level simulation in the foreseeable future and, therefore, calculations of thermal transport must rely on solutions of the Boltzmann transport equation; microscopic phonon scattering rates needed for predictive models are, even for Si, poorly known. Low-dimensional nanostructures, such as carbon nanotubes, are predicted to have novel transport properties; the first quantitative experiments of the thermal conductivity of nanotubes have recently been achieved using microfabricated measurement systems. Nanoscale porosity decreases the permittivity of amorphous dielectrics but porosity also strongly decreases the thermal conductivity. The promise of improved thermoelectric materials and problems of thermal management of optoelectronic devices have stimulated extensive studies of semiconductor superlattices; agreement between experiment and theory is generally poor. Advances in measurement methods, e.g., the 3ω method, time-domain thermoreflectance, sources of coherent phonons, microfabricated test structures, and the scanning thermal microscope, are enabling new capabilities for nanoscale thermal metrology. © 2003 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70161/2/JAPIAU-93-2-793-1.pd

Uncertainty Analysis and Control of Multiscale Process Systems

Microelectronic market imposes tight requirements upon thin film properties, including specific growth rate, surface roughness and thickness of the film. In the thin film deposition process, the microscopic events determine the configuration of the thin film surface while manipulating variables at the macroscopic level, such as bulk precursor mole fraction and substrate temperature, are essential to product quality. Despite the extensive body of research on control and optimization in this process, there is still a significant discrepancy between the expected performance and the actual yield that can be accomplished employing existing methodologies. This gap is mainly related to the complexities associated with the multiscale nature of the thin film deposition process, lack of practical online in-situ sensors at the fine-scale level, and uncertainties in the mechanisms and parameters of the system. The main goal of this research is developing robust control and optimization strategies for this process while uncertainty analysis is performed using power series expansion (PSE). The deposition process is a batch process where the measurements are available at the end of the batch; accordingly, optimization and control approaches that do not need to access online fine-scale measurements are required. In this research, offline optimization is performed to obtain the optimal temperature profile that results in specific product quality characteristics in the presence of model-plant mismatch. To provide a computationally tractable optimization, the sensitivities in PSEs are numerically evaluated using reduced-order lattices in the KMC models. A comparison between bounded and distributional parametric uncertainties has illustrated that inaccurate assumption for uncertainty description can lead to economic losses in the process. To accelerate the sensitivity analysis of the process, an algorithm has been presented to determine the upper and lower bounds on the outputs through distributions of the microscopic events. In this approach, the sensitivities in the series expansions of events are analytically evaluated. Current multiscale models are not available in closed-form and are computationally prohibitive for online applications. Thus, closed-form models have been developed in this research to predict the control objectives efficiently for online control applications in the presence of model-plant mismatch. The robust performance is quantified by estimates of the distributions of the controlled variables employing PSEs. Since these models can efficiently predict the controlled outputs, they can either be used as an estimator for feedback control purposes in the lack of sensors, or as a basis to design a nonlinear model predictive control (NMPC) framework. Although the recently introduced optical in-situ sensors have motivated the development of feedback control in the thin film deposition process, their application is still limited in practice. Thus, a multivariable robust estimator has been developed to estimate the surface roughness and growth rate based on the substrate temperature and bulk precursor mole fraction. To ensure that the control objective is met in the presence of model-plant mismatch, the robust estimator is designed such that it predicts the upper bound on the process output. The estimator is coupled with traditional feedback controllers to provide a robust feedback control in the lack of online measurements. In addition, a robust NMPC application for the thin film deposition process was developed. The NMPC makes use of closed-from models, which has been identified offline to predict the controlled outputs at a predefined specific probability. The shrinking horizon NMPC minimizes the final roughness, while satisfying the constraints on the control actions and film thickness at the end of the deposition process. Since the identification is performed for a fixed confidence level, hard constraints are defined for thin film properties. To improve the robust performance of NMPC using soft constraints, a closed-form model has been developed to estimate the first and second- order statistical moments of the thin film properties under uncertainty in the multiscale model parameters. Employing this model, the surface roughness and film thickness can be estimated at a desired probability limit during the deposition. Thus, an NMPC framework is devised that successfully minimizes the surface roughness at the end of the batch, while the film thickness meets a minimum specification at a desired probability. Therefore, the methods developed in this research enable accurate online control of the key properties of a multiscale system in the presence of model-plant mismatch

Kardar-Parisi-Zhang Universality, Anomalous Scaling and Crossover Effects in the Growth of Cdte Thin Films

We report on the growth dynamic of CdTe thin films for deposition temperatures ($T$) in the range of 150\,^{\circ}\mathrm{C} to 300\,^{\circ}\mathrm{C}. A relation between mound evolution and large-wavelength fluctuations at CdTe surface has been established. One finds that short-length scales are dictated by an interplay between the effects of the formation of defects at boundaries of neighbouring grains and a relaxation process which stems from the diffusion and deposition of particles (CdTe molecules) torward these regions. A Kinetic Monte Carlo model corroborates these reasonings. As $T$ is increased, the competition gives rise to different scenarios in the roughening scaling such as: uncorrelated growth, a crossover from random to correlated growth and transient anomalous scaling. In particular, for T = 250\,^{\circ}\mathrm{C}, one shows that surface fluctuations are described by the celebrated Kardar-Parisi-Zhang (KPZ) equation, in the meantime that, the universality of height, local roughness and maximal height distributions for the KPZ class is, finally, experimentally demonstrated. The dynamic of fluctuations at the CdTe surface for other temperatures still is described by the KPZ equation, but with different values for the superficial tension ($\nu$) and excess of velocity ($\lambda$). Namely, for T = 150\,^{\circ}\mathrm{C} one finds a Poissonian growth that indicates $\nu = \lambda = 0$. For T = 200\,^{\circ}\mathrm{C}, however, a Random-to-KPZ crossover is found, with $\lambda > 0$ in the second regime. Finally, for films grown at T = 300\,^{\circ}\mathrm{C}, one demonstrates that a KPZ growth with $\lambda < 0$ takes place. We discuss the different mechanisms leading to KPZ scaling which depend on $T$, and conjecture the behavior of the phenomenological parameter $\lambda$ as function of the deposition temperature.Comment: 117 pages, 46 figures, Dissertation Thesi

Fabrication and characterization of a Magnetic Tunnel Transistor with an epitaxial spin valve by the shadow mask technique

This work is concerned with the development of a fabrication method for Magnetic Tunnel Transistor (MTT) having an epitaxial spin valve. Over the years, this device has been used for the study of spin polarized hot electron transport in thin films. Further, the high on/off ratio that the use of thin magnetic films enables makes this device interesting for industrial application. One of the main limitations of this device however is the low ratio between the injected current and the collected current. In this work, use of epitaxial layers has been made so as to decrease the scattering probability of hot electrons, thereby increasing the aforementioned ratio. In order to ensure a high fabrication throughput as well as reduce the amount of lithography induced defects, a shadow mask based deposition method has been successfully developed. This thesis first describes the theoretical framework of the spin polarized hot electron transport in the MTT and investigates using a Monte Carlo algorithm the influence of structural defects, bandstructure as well temperature on the different characteristic parameters of the MTT. The fabrication method is subsequently describes and finally the experimental results are discussed in light of the theoretical prediction of the model as well as of the previous experimental reports on MTTs

A Kinetic Monte Carlo Study of Mesoscopic Perovskite Solar Cell Performance Behavior

Perovskite solar cells have received considerable attention in recent years due to their low processing cost and high energy conversion efficiency. However, the mechanisms of perovskite solar cell performance are not fully understood. Models based on probabilistic and statistical approaches can be used to simulate, optimize, and predict perovskite solar cell photovoltaic performance, and they can also guide experimental processing and fabrication conditions to achieve higher photovoltaic efficiency. This work developed a 3D model based on the kinetic Monte Carlo (KMC) approach to simulate 3D morphology of perovskite-based solar cells and predict their photovoltaic performance. The model incorporated the physical behavior of perovskite cells with respect to their charge generation, transport, and recombination characteristics. KMC simulation results showed that perovskite films with the pin holes-free and a homogenous perovskite capping layer of 400 nm thickness produced a maximum photovoltaic efficiency of 20.85%, resulting in minimal charge transport time (τt) and maximum charge carrier recombination lifetime (τr). Photovoltaic performance from the fabricated device has been used to validate this simulation model. This model provides significant conceptual advances in identifying current performance constraints and guiding novel device designs that enhance overall perovskite photovoltaic performance

OXIDATION OF SILICON - THE VLSI GATE DIELECTRIC

Silicon dominates the semiconductor industry for good reasons. One factor is the stable, easily formed, insulating oxide, which aids high performance and allows practical processing. How well can these virtues survive as new demands are made on integrity, on smallness of feature sizes and other dimensions, and on constraints on processing and manufacturing methods? These demands make it critical to identify, quantify and predict the key controlling growth and defect processes on an atomic scale.The combination of theory and novel experiments (isotope methods, electronic noise, spin resonance, pulsed laser atom probes and other desorption methods, and especially scanning tunnelling or atomic force microscopies) provide tools whose impact on models is just being appreciated. We discuss the current unified model for silicon oxidation, which goes beyond the traditional descriptions of kinetic and ellipsometric data by explicitly addressing the issues raised in isotope experiments. The framework is still the Deal-Grove model, which provides a phenomenology to describe the major regimes of behaviour, and gives a base from which the substantial deviations can be characterized. In this model, growth is limited by diffusion and interfacial reactions operating in series. The deviations from Deal-Grove are most significant for just those first tens of atomic layers of oxide which are critical for the ultra-thin oxide layers now demanded. Several features emerge as important. First is the role of stress and stress relaxation. Second is the nature of the oxide closest to the Si, both its defects and its differences from the amorphous stoichiometric oxide further out, whether in composition, in network topology, or otherwise. Thirdly, we must consider the charge states of both fixed and mobile species. In thin films with very different dielectric constants, image terms can be important; these terms affect interpretation of spectroscopies, the injection of oxidant species and relative defect stabilities. This has added importance now that P-b concentrations have been correlated with interfacial stress. This raises further issues about the perfection of the oxide random network and the incorporation of interstitial species like molecular oxygen.Finally, the roles of contamination, particles, metals, hydrocarbons etc are important, as is interface roughness. These features depend on pre-gate oxide cleaning and define the Si surface that is to be oxidized which may have an influence on the features listed above