Non-volatile memory (NVM) is a form of computer memory in which the logical value (1 or 0) of a bit is retained when the computer is in its’ powered off state. Flash memory is a major form of NVM found in many computer-based technologies today, from portable solid state drives to numerous types of electronic devices. The popularity of flash memory is due in part to the successful development and commercialization of the floating gate transistor. However, as the floating gate transistor reaches its’ limits of performance and scalability, viable alternatives are being aggressively researched and developed. One such alternative is a memristor-based memory application often referred to as ReRAM or RRAM (Resistive Random Access Memory). A memristor (memory resistor) is a passive circuit element that exhibits programmable resistance when subjected to appropriate current levels. A high resistance state in the memristor corresponds to a logical ‘0’, while the low resistance state corresponds to a logical ‘1’. One memristive system currently being actively investigated is the metal/metal oxide/metal material stack in which the metal layers serve as contact electrodes for the memristor with the metal oxide providing the variable resistance functionality. Application of an appropriate potential difference across the electrodes creates oxygen vacancies throughout the thickness of the metal oxide layer, resulting in the formation of filaments of metal ions which span the metal oxide, allowing for electronic conduction through the stack. Creation and disruption of the filaments correspond to low and high resistance states in the memristor, respectively. For some time now, HfO2 has been researched and developed to serve as a high-k material for use in high performance CMOS MOSFETs. As it happens, HfO2-based RRAM devices have proven themselves as viable candidates for NVM as well, demonstrating high switching speed (< 10 ns), large OFF/ON ratio (> 100), good endurance (> 106 cycles), long lifetime, and multi-bit storage capabilities. HfO2-based RRAM is also highly scalable, having been fabricated in cells as small as 10 x 10 nm2 while still maintaining good performance. Previous work examining switching properties of micron scale HfO2-based RRAM has been performed by the Vogel group. However, a viable process for fabrication of nano-scale RRAM is required in order to continue these studies. In this work, a fabrication process for nano-scale cross-point TiN/ HfO2/TiN RRAM devices will be developed and described. Materials processing challenges will be addressed. The switching performance of devices fabricated by this process will be compared to the performance of similar devices from the literature in order to confirm process viability.

Product densities have been widely used in the literature to give a concrete description of the distribution of a point process. A rigorous description of properties of product densities is presented with examples to show that in some sense these results are the best possible. Product densities are then used to discuss positive dependence properties of point processes. There are many ways of describing positive dependence. Two well known notions for Bernoulli random variables are the strong FKG inequalities and association, the strong FKG inequalities being much stronger. It is known, for example, from van den Berg and Burton, that the strong FKG inequalities are equivalent to all conditional distributions being associated, which is equivalent to all conditional distributions being positively correlated. In the case of point processes for which product densities exist, analogs of such positive dependence properties are given. Examples are presented to show that unlike the Bernoulli case none of these conditions are equivalent, although some are shown to be implied by others. / Graduation date: 1988

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"In electronic structure computations, it is necessary to set up and solve a certain nonlinear eigenvalue problem to identify materials. In this dissertation, we first introduce the nonlinear eigenvalue problem and the currently prevailing Self-Consistent Field (SCF) method accelerated by the Anderson acceleration method. We then compare the Anderson acceleration method with the well-known Generalized Minimal Residual (GMRES) method and show that they are essentially equivalent when applied to linear systems. After that, we study a linearly constrained least-squares problem embedded in the Anderson procedure. We use numerical experiments to illustrate the convergence properties. Finally, we give a summary of our work and an outline of future research."

As-Is Building Information Models aid in the management, maintenance and renovation of existing buildings. However, most existing buildings do not have an accurate geometric depiction of their As-Is conditions. The process of generating As-Is models of existing structures involves practitioners, who manually convert Point Cloud Data (PCD) into semantically meaningful 3D models. This process requires a significant amount of manual effort and time. Previous research has been able to model objects by segmenting the point clouds into planes and classifying each one separately into classes, such as walls, floors and ceilings; this is insufficient for modelling, as BIM objects are comprised of multiple planes that form volumetric objects. This thesis introduces a novel method that focuses on the geometric creation of As-Is BIMs with enriched information. It tackles the problem by detecting objects, modelling them and enriching the model with spaces and object adjacencies from PCD. The first step of the proposed method detects objects by exploiting the relationships the segments should satisfy to be grouped into one object. It further proposes a method for detecting slabs with variations in height by finding local maxima in the point density. The second step models the geometry of walls and finally enriches the model with closed spaces encoded in the Industry Foundation Classes (IFC) standard. The method uses the point cloud density of detected walls to determine their width by projecting the wall into two directions and finding the edges with the highest density. It identifies adjacent walls by finding gaps or intersections between walls and exploits walls adjacency for correcting their boundaries, creating an accurate 3D geometry of the model. Finally, the method detects closed spaces by using a shortest-path algorithm. The method was tested on three original PCD which represent office floors. The method detects objects of class walls, floors and ceilings in PCD with an accuracy of approximately 96%. The precision and recall for the room detection were found to be 100%.

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