The research primarily focuses on the development and fabrication of 4H-Silicon Carbide (SiC) power devices. As of today, power devices play a substantial role in high power applications such as fast-charging stations for electric vehicles, inverters for solar power, and energy storage equipment, to name a few. To minimize power loss during the operation, one of the key elements is to develop an energy-efficient power device. Although silicon (Si)-based power devices are currently being used in various high power applications, Si reached its physical limit in power loss reduction. In this aspect, wide-bandgap material, especially 4H-Silicon Carbide (SiC), became an excellent candidate to replace Si to fabricate power semiconductor devices that enable further minimization of power dissipation beyond Si. To advance the present and future low voltage (600 V) and high voltage (15 kV) power applications, the development of both low voltage and high voltage power devices are imperative. The most unique feature of a power device is the ability to withstand high voltages (> 600 V) with a voltage supporting layer, called the “drift region”. The breakdown voltage of the power device depends on the thickness and doping concentration of the drift region, as most of the voltage is supported by the depletion region formed within the drift layer. The optimization of the drift region must be performed to meet the breakdown voltage requirements based on the application while minimizing the on-state voltage drop to reduce power dissipation. When compared to the Si counterparts, SiC allows for the design of a thin, heavily doped drift region to support a specified voltage due to its superior material properties. Additionally, leakage currents generated during the off-state mode are also significantly suppressed due to two orders of magnitude lower intrinsic carrier density than that of Si. These merits of SiC become more substantial when building high voltage power devices (>3.3 kV) where resistance in the drift region dominates the overall on-resistance of the device. The details of optimizing device structures, fabrication details, and electrical characterizations of 600 V to 15 kV 4H-SiC power devices are discussed in this dissertation. The fundamental of the power device including the design of the drift layer and edge termination techniques for the power device will be discussed. To improve the low voltage application (i.e. electrical vehicles and photovoltaic converters), 600 V-rated lateral and vertical MOSFETs were developed and fabricated. From this work, the world's first high current (10 A) and high voltage (600 V) SiC lateral MOSFET was demonstrated. The fabricated lateral MOSFET was compared with the state-of-the-art vertical power MOSFET to identify the performance gaps to further enhance the electrical performances of the lateral MOSFETs. 600 V vertical MOSFETs and JBSFET (Junction-Barrier-Schottky (JBS) diode integrated MOSFET) were also developed to reduce the power loss in the system by replacing the Si-IGBTs (insulated-gate-bipolar-transistor) in the circuitry. The utilization of unipolar devices (i.e. MOSFET) is often more favorable than the bipolar devices (i.e. IGBT) due to faster switching speed and lower switching loss. On the other hand, the development of high voltage (> 6.5kV) devices are essential for high power applications such as power grids, military vehicles, to name a few. The fabrication and application of single-chip, high voltage devices are advantageous in terms of replacing many series-connected devices used to withstand high voltage in power circuits. However, research on ≥ 6.5kV-rated 4H-SiC power devices are very limited. With this motivation, 6.5 kV to 15 kV SiC JBS diodes, MOSFETs, and JBSFETs were designed and fabricated. From this study, we identified that device optimization for high voltage (> 6.5 kV) devices are different from the low voltage (< 1700V) devices due low background doping concentration of high voltage devices. Critical design considerations for fabricating 6.5 kV to 15 kV devices will be discussed. Both static and dynamic characteristics were also evaluated and compared, respectively.

Protein Tyrosine phosphatase 1B (PTP1B), is an endoplasmic resident protein and a well-known negative regulator of the insulin receptor, dephosphorylating Tyr-1162, and Tyr-1163, two residues located in the activation loop of the insulin receptor. Mice lacking the PTPN1 gene encoding for PTP1B exhibit increased insulin sensitivity and improved glucose tolerance. Apart from its role in insulin signaling, mice lacking PTP1B show resistance to weight gain on a highfat diet, increased basal metabolic rate, and decreased cholesterol levels. In addition, PTP1B was previously identified in a proteome-wide mapping of cholesterol-interacting proteins in mammalian cells. However, the relationship between PTP1B and cholesterol is still unclear. To better understand the role of cholesterol on PTP1B function and on insulin signaling, we first used an in silico approach to predict cholesterol-binding sites in the 3D structure of the phosphatase and confirmed the binding sites through fluorescence binding studies and mass fingerprinting. We confirmed that the association between PTP1B and cholesterol occurred in both in vitro and in mammalian cells. In an attempt to understand whether cholesterol affects the ability of PTP1B to dephosphorylate substrates, we performed activity assays in various conditions. We observed that cholesterol could reduce and reactivate the reversibly oxidized form of PTP1B in vitro. Treatment of mammalian cells with cholesterol confirmed that excess cholesterol kept PTP1B reduced, and decreased Insulin Receptor phosphorylation and downstream signaling. In vivo results obtained by exposing mice to a high cholesterol diet support a role in the cholesterol-mediated reduction of PTP1B and decreased insulin sensitivity in the liver. We have established an electron tunneling path between the allosteric site and the catalytic cysteine residue and used a redox-sensitive fluorophore to measure electron tunneling in vitro. Hence, our results demonstrate for the first time that cholesterol binds to PTP1B at an allosteric site and reduces the phosphatase to regulate its activity and insulin signaling. Based on these results we propose a novel role for cholesterol in activating enzymes and in the context of insulin resistance.

Despite decades of research and billions of dollars in funding, cancer has maintained its epidemiological prominence as the second leading cause of death in the US for nearly 90 years. Currently, the clinical trial success rates for oncologic drugs is ~3%, and approved drugs often have a modest impact on overall survival. This is due in part to the tumor microenvironment (TME) which promotes cancer development and mitigates therapeutic response. Study of this biological system, however, is limited by conventional in vitro and in vivo techniques, which compromise either physiological relevance or experimental control. To better understand the role of the TME, we have utilized microfabrication techniques to develop the microfluidic imaging window (MFIW), an implantable platform for the observation and manipulation of in vivo TMEs. This technology provides unique opportunities for assessing the pharmacologic effects of therapeutics within intact, living tissue. Among the applications explored, a novel photolithographic technique, termed post exposure lamination, was developed to integrate tapered SU-8 micro-nozzle structures and enhance fluid conduction into porous matrices. Using these features, it was found that micro-nozzles improved axial penetration of fluorescent dextran into agarose tissue mimics and reduced the radial dispersion of Trypan Blue dye. Applications of localized reagent delivery for enhanced assay control were also investigated using small molecule nuclear stains and cell-based reporter systems. Here, significant cell staining occurred rapidly using small volumes of reagent (100 nL), substrate delivery for enzymatic processing was detected using a bioluminescent readout, and induction of cell gene expression was used to upregulate the production of fluorescent protein. Collectively, these capabilities showcase applications of the MFIW for enhanced monitoring and modulation of the TME that are well suited for translation into in vivo animal studies.

This work presents multiple Long Short-Term Memory neural networks used in con- junction with sentiment analysis to predict stock prices over time. Multiple datasets and input features are used on a LSTM model to decipher which features produce the best output predictions and if there is correlation to the sentiment of posts and the rising of a stock. This project uses embedding based sentiment analysis on a dataset collected from Kaggle which includes over one million posts made on the subreddit r/wallstreetbets. This subreddit recently came under fire by the media with the shorting of Gamestop in the stock market. It was theorized that this subreddit was working as a collective to drive up the price of multiple stock, therefore hurting large corporations such as hedge funds that had large short positions on multiple stocks.

The following document is meant to represent an overview of my work on structurally interacting RNA (sxRNA), which has already resulted in three publications with another two in preparation. Where appropriate, some text and data from these publications have been reproduced here. Ribonucleic Acid (RNA) is one of the fundamental macromolecules present in living systems. It can be found in all cells as varying length polymer chains composed of four primary bases (adenine, cytosine, guanine, uracil) capable of numerous modifications. Though generally characterized as an information carrier, RNA is a versatile molecule that exhibits both intra and inter-strand base pairing to form complex structures. Similar to protein, the particular shape of an RNA structure in combination with some degree of sequence specificity, can dictate its function (RNA binding protein recognition sites, ribozyme activity, aptameric affinity, etc.). Structurally interacting RNA (sxRNA) is a molecular switch technology that exploits predictable intermolecular RNA base pairing to form an otherwise absent functional structure in one RNA strand when it interacts with a specific, targeted second strand. Originally proposed as a potential regulatory mechanism in natural systems, we used characteristics of predicted pairings in that context to engineer purely synthetic sxRNA switches that have been successfully tested. There are many non-coding RNAs associated with pathological conditions, the ability to use these as triggers for sxRNA opens the door to potential applications ranging from diagnostics to therapeutics. Furthermore, other prospective triggers (including those synthetically designed) may allow use of the technology as a molecular tool for a variety of purposes including as an alternative to antibiotic selection in cell line development. The typical trigger sequences targeted by sxRNA switches are at least 20 bases in length. Combinatorial options with regard to structure positioning and base composition produce an enormous number of potential sxRNA sequences for any given target. Exhaustively examining these for feasible candidates (i.e., analyzing predicted interactions with unintended targets) is computationally impossible with current systems. Evolutionary computing is a subfield of artificial intelligence (AI) that has been inspired by biology. Genetic algorithms are a type of evolutionary algorithm and apply operators (such as recombination and mutation) to find candidate solutions to an optimization problem. The presented dissertation will describe the original sxRNA research as well as the development and testing of a genetic algorithm that automates the production of new sxRNA switch candidates. This algorithm takes into consideration factors that were previously impossible to account for in manual designs.

Insulators play an important role in the architecture and resulting performance of semiconductor devices manufactured today. Materials such as HfO2 and Al2O3 are utilized as gate oxides and spacers to control leakage current and enable bottom-up self-aligned patterning of device features. Understanding the electrostatic barrier that forms at the metal-oxide-semiconductor (MOS) interface is crucial in the development of field effect transistors and other devices, especially as the scaling of device features continues to shrink into the nanoscale. Characterization of the barrier height using current-voltage (IV) and capacitance-voltage (CV) techniques provides only a spatially averaged view of the interface, and is incapable of accounting for local nonuniformity which arises at nanoscale dimensions. Additionally, common lithographic strategies for patterning small feature oxides are limited by printing misalignments such as edge placement error (EPE), and in order to achieve smaller pitch sizes lithography steps must be repeated multiple times which adds time and cost to the process. The feasibility of uniform, cost-effective insulator films at the 5 nm technology node and beyond relies on the development of new deposition strategies. In this thesis, hafnium oxide grown using atomic layer deposition (ALD) is examined with ballistic electron emission microscopy (BEEM). Localized nonuniformities in the barrier height are found to exist for two identically prepared samples which reveal three distinct electrostatic barriers at the buried Au/HfO2/SiO2/Si-p interface, including a novel barrier found at 0.45 eV due to ultrathin HfO2. The results uncover changes in electrostatic behavior of the film which are otherwise impossible to detect using spatially averaged techniques. These variations in barrier height are visualized in a novel way that produces spatial maps showing transitions between high energy and lower energy barriers across a few nanometers. The resolution of this mapping technique is determined by comparing the measured barrier heights of Au/Si(001) and Au/Si(111) interfaces. Momentum conservation and electron scattering result in slightly different barrier heights for both interfaces that depends on metal thickness. The Rayleigh criterion is applied to the barrier height distributions as a function of metal thickness, resulting in a 10 meV resolution. Both aluminum oxide and hafnium oxide are also selectively grown on patterned metal / low-k silicon wafers using ALD. Self-assembled monolayer (SAM) materials such as octodecanethiol (ODT) and dodecanethiol (DDT) -which are functionalized to metal -are first deposited on the copper lines in order to block high-k film deposi¬tion on metal. Both HfO2 and Al2O3 are shown to selectively cover the low-k lines for linespace pitches greater than 100 nm and 5 mM concentration of SAM, and better selectivity is achieved for smaller pitches using lower SAM concentrations. Selectivity is measured qualitatively and quantitatively using x-ray photoemission spectroscopy and confirmed with transmission electron microscopy.

Cerium oxide or ceria has garnered a wide range of applications due to its redox active nature. This redox activity is due to oxygen vacancies on the surface of the ceria creating a layer of mixed oxide with the unstable oxide Ce2O3 (Ce[superscript 3+]) present at the same time as the bulk oxide CeO2 (Ce[superscript 4+]). Possible applications for ceria include water splitting, oxidation of carbon monoxide, oxidation of reactive oxygen species and polishing of glass films. In recent years, ceria nanoparticles have been used for polishing thermal silicon oxide during the early steps of semiconductor fabrication in a process referred to as chemical mechanical planarization (CMP). The advantage of these particles is their ability to abrade an oxide surface chemically using the aforementioned redox properties, as well as mechanically. To meet the needs of manufacturing, mainly removal rate and surface roughness, the particles used must have well controlled physical properties such as size and shape for mechanical removal and ratio of cerium oxidation state for chemical removal. This study encompasses three parts following the design of ceria slurries, their implementation in the existing silicon oxide polish and applying these findings to create novel slurries for polishing metals. To design ceria slurry, the ratio of Ce[superscript 3+]/Ce[superscript 4+] on the surface of abrasive was maximized by altering the slurries’ chemical environment. Maximizing this ratio increases the proportion of active Ce[superscript 3+] sites which participate in removal reactions. The effect of chemical environment on the Ce[superscript 3+]/Ce[superscript 4+] ratio was determined through XPS analysis of the Ce 3d spectrum. The knowledge gained in this first section informed the design of ceria slurries for the following two parts to maximize their effectiveness. The second part of this thesis applies this knowledge to create ceria iv slurries that polished thermal oxide with higher material removal rate (MRR) and lower postpolish roughness than slurries that are currently being used in industry. The basis of ceria polishing is known as the tooth-comb model. In this model oxygen at Ce[superscript 3+] sites will undergo a condensation reaction with oxygen on the surface to be polished. As the particle leaves this will rip material off of the wafer surface. While the tooth-comb model was proposed for polishing silica, the final part of this thesis seeks to generalize it to encompass polishing any oxide given the correct conditions. To demonstrate this, I created ceria slurries to polish metals relevant to the semiconductor industry (copper, tungsten and ruthenium) with polishing metrics that equal or exceed those of industry standard slurries.

Computer architectures inspired by biological neural networks are currently an area of growing interest, due to immense utility of these systems which is shown by their near-ubiquity within animals. An essential aspect of these systems is their ability to compute through the exchange of temporal events called ‘spikes.’ However, many aspects of biological computation remain unknown. To improve our ability to measure neural systems, we create an efficient implementation and statistical testing method to calculate an information-theory based metric, transfer entropy, on signals recorded from cultures of neurons. Taking inspiration from established knowledge regarding biological neurons, we investigate the impact which stochastic behavior has on the robustness of spiking networks when their synaptic weights are inaccurate. We find that a level of stochasticity can help improve this robustness. Lastly, we investigate methods of creating programs for spike-based computation through evolutionary optimization methods, and identify opportunities and challenges in this area.

Understanding the properties and performance of semiconductor interfaces on the nanoscale advances semiconductor device technology which has had tremendous impact on nearly all aspects of our daily lives. Investigating the nanoscale fluctuations in the electrostatics of metal-semiconductor, or Schottky, interfaces is crucial. However, techniques for directly measuring the electrostatics at an interface are limited. Current state-of-the-art finFETs use metal-semiconductor silicides, such as Ti-Si/Si, for Schottky source-drain contacts. Studying the underlying physics of the Schottky barrier interface of silicides and other metal-semiconductor systems is critical for measuring the Schottky barrier accurately, which can be accomplished with ballistic electron emission microscopy (BEEM), a scanning tunneling microscopy (STM) based technique. In this work, the visualization of the interface to nanoscale dimensions is enhanced by computational modelling of threshold histograms acquired by the BEEM measurement technique. Modelling using a kinetic Monte-Carlo approach is utilized to simulate the distributions of barrier heights that includes effects from the interface and transport of the hot electrons as well as indication of a multi-barrier heights present at the interface. The aid of this modelling enables the discovery of several underlying properties of the interface. Analyzing the parameters of the modelling and comparing to measured data provides detailed insight into the effects that both electron scattering and incomplete silicide formation in W/Si(001) and WSi2/Si(001) have upon the transport of electrons through these structures, which is difficult to detect with conventional current-voltage measurements. The modelling also includes simulation of multiple barriers present at the interface due to the intermixing of similar metals such as Au and Ag at the interface of Si(001) In this regard, Schottky barrier visualization as the combination of histograms, mapping, and modelling provides a new insight into the local nanoscale phenomenon of the Schottky barrier. This thesis investigates the modelling of these metal-semiconductor systems and uses modelling to look at metal thickness dependent effects on the Schottky barrier from Fermi-level pinning in Au/Cr-Si/Si(001) and Au/Cr-Si/Si(111) silicide.

Salivary glands are essential in maintaining oral cavity homeostasis. This tissue can become impaired by chemotherapy/radiotherapy given to head and neck cancer patients, as well as systemic diseases. Once this gland is damaged, it has limited ability to regenerate, and so the need for potential biodegradable/biocompatible scaffolds to aid in the growth and repair is of great interest. This soft tissue is made up of multiple cell populations that contribute to the function of the gland. Creating an environment that can recapitulate the one seen in vivo will promote the functionality of the engineered tissue. This research aims to investigate: (1) cell-substrate interactions with salivary gland epithelial cells and nanofiber scaffolds, (2) cell-cell interactions via incorporation of a second native cell population to further enhance epithelial differentiation, mimicking the in vivo microenvironment and (3) the development of engineering a three-dimensional scaffold that will better facilitate the two interactions described above. The hypothesis is that sponge scaffolds that mimic the mechanical properties and architecture of the tissue observed in vivo will provide a platform for future implantation and regeneration strategies. Bio-mimetically engineered scaffold systems for the growth of organs, such as the one described here, yield novel tools for studying organ development in applications for regenerative medicine.