This research primarily focuses on the design, fabrication, and characterization of 1.2 kV 4H-SiC devices. Power devices play a critical role in numerous high-power applications, including automotive, industrial and energy applications. The development of energy-efficient power devices is essential for reducing power loss during operation. While silicon-based power devices are widely used in high-power applications, they have reached their limit in minimizing power loss. As a result, wide-bandgap materials, particularly 4H-Silicon Carbide (SiC), have gained traction as replacements for their Silicon counterparts due to their superior material properties, enabling further reduction of power dissipation beyond Si. The demand for 1.2 kV 4H-SiC MOSFETs has significantly increased, particularly in the electric vehicle (EV) market where high performance, reliability, and ruggedness are critical to compete with Si counterparts. Hence, the optimization of 1.2 kV 4H-SiC devices is necessary. The most distinctive feature of a power device is its ability to withstand high voltages within the drift region. The breakdown voltage of the power device is determined by the specifications of the drift region. The optimization of the drift region must be performed to enhance power efficiency for each specific application due to the trade-off relationship between on-resistance and breakdown voltage. 4H-SiC enables a thin, heavily doped drift region to support a given breakdown voltage, resulting in a substantial reduction in the on-resistance of the device compared to Si. Moreover, Hybrid Junction Termination Extension (Hybrid-JTE) technique was employed to achieve a near-ideal breakdown voltage and experimentally verified. The influence of deep JFET and P-well implants in 1.2 kV MOSFETs has been examined in terms of their impact on static characteristics and short-circuit ruggedness. To assess the impact on output characteristics and short-circuit ruggedness, the depths of JFET and P-well implants were compared by varying channel lengths and JFET widths. Furthermore, the significance of high channel mobility has been investigated not only for static characteristics but also for short-circuit characteristics. The optimization of static characteristics of 1.2 kV 4H-SiC MOSFETs have been investigated through the analysis of the cell structure. A comprehensive analysis has been conducted to examine the trade-off relationship between specific on-resistance and breakdown voltage, as well as yield, by considering various dimensions within the cell structure. The dimensions explored in this analysis include the channel, JFET, contact opening, ILD (Inter-Layer Dielectric) width, and gate-to-source overlap within the cell structure. A novel approach has been proposed to enhance the trade-off relationship between short-circuit withstand time and specific on-resistance by employing MOSFETs with a deep P-well structure through channeling implantation. For the channeling implantation, a tilt angle of 4 degrees was adjusted to <0001> direction of 4H-SiC in 4H-SiC (0001) substrates with 4 ° off-cut towered <11-20> direction. The utilization of channeling implantation has been employed to overcome the limitations associated with previous random implantation energy. The successful fabrication and demonstration of MOSFETs with deep P-well structures using channeling implantation have been achieved. The MOSFETs with a deep P-well structure enable the short channel lengths, which improve the trade-off relationship between specific on-resistance and breakdown voltage. The implementation of a deep P-well structure effectively suppresses the leakage current originating from the channel during the blocking-mode of operation, thereby enhancing the trade-off relationship. Additionally, the deep P-well structure has significantly reduced the maximum electric field in the gate oxide, leading to improved high temperature reverse bias (HTRB) characteristics. A novel layout approach has been proposed and successfully demonstrated for the monolithic integration of a Schottky diode with 1.2 kV SiC MOSFETs (JBSFETs) to achieve an identical cell pitch compared to the pure MOSFET design. To further reduce cell density, highly doped JFET implantation with narrow widths of JFET/Schottky regions has been conducted. Consequently, the proposed JBSFET demonstrates comparable static performance to the pure MOSFET while exhibiting 3rd quadrant current-voltage characteristics similar to JBS diodes. A thorough comparison of the short-circuit failure mechanisms between 1.2 kV 4H-SiC MOSFETs and Ti JBSFETs, both having identical cell pitch and specific on-resistance, has been successfully accomplished. However, despite the same channel density, different short-circuit characteristics have been observed due to the presence of leakage current from the Schottky contact in the JBSFETs. In order to comprehend the short-circuit failure mechanisms, non-isothermal mixed-mode 2D TCAD device simulations have been employed. Moreover, based on the experimental results and analyses, potential solutions to further enhance the short-circuit characteristics of JBSFETs have been proposed. A 1.2 kV 4H-SiC planar Junction Barrier Schottky (JBS) diode with a deep P+ grid structure, implemented through channeling implantation, has been successfully designed and fabricated. Without the use of a trench structure, a planar JBS diode with a junction depth of 2.2 μm has been successfully fabricated using an implantation energy of 350 keV. The formation of the deep junction significantly suppressed the leakage current originating from the Schottky contact. In summary, extensive examinations have been conducted on 1.2 kV rated 4H-SiC power devices, including MOSFETs, JBSFETs, and JBS diodes, to optimize and enhance their static characteristics, dynamic characteristics, reliability, and ruggedness.

Additive manufacturing has several advantages over conventional subtractive fabrication techniques such as CNC machining, including limited waste and the ability to build complicated structures using fewer processing steps. Recently, a cost-effective, versatile method of high-resolution printing called electrohydrodynamic printing has been developed. With this technique, droplets are deposited from a tip by applying an external electric field. This method allows for spatial resolution in the hundreds of nanometers when used in a drop-on-demand mode of operation. However, this mode has the drawback of relatively slow deposition rates. To increase the rate of deposition, it is desired to deposit the droplets using a continuous spray, which is called the cone jet mode. However, this mode of operation has a much lower resolution because of the space charge effect (lateral spread of the stream of charged droplets from Coulomb repulsion). The primary goal of this research project was to develop an electrohydrodynamic printing system with a focusing element to allow printing at much higher deposition rates than the drop-on-demand mode while maintaining reasonably high lateral resolution. After modeling different focusing systems, an Einzel lens was determined to be the optimal focusing element. A custom electrohydrodynamic printing system with a three-element Einzel lens was fabricated and tested with 10% polyethylene glycol solution. Droplets with a lateral diameter of ~1 μm after drying were successfully deposited. The incorporation of the Einzel lens resulted in an order of magnitude improvement in lateral resolution of the spray.

Conventional von Neumann computing architecture has little parallelism and physically divides memory and logic units. In-memory computation offers further opportunity for power and efficiency improvements as data transport between memory and logic units introduces substantial power consumption and latency. RRAM memory devices are a non-volatile, highly scalable alternative that is compatible with advanced logic CMOS processes. Because of its in-memory computing capabilities, it is a more appealing solution for data-intensive applications. Depending on the needs of the application, RRAM's different switching types such as binary switching, multilayer switching, and analog switching can be employed. These devices can be utilized in applications where they must continuously update their condition (e.g neural network training). The RRAM memory system can be of great use in applications where the device must continuously maintain the resistance state as well, either with or without in-memory processing. Moreover, RRAM devices are currently being researched for in-sensor or near-sensor applications to speed up AI inference. The diversity of RRAM switching schemes and application range creates vast research opportunities to examine the viability of these devices. Hence, leveraging RRAM’s unique switching properties analog (continuous) or binary/multilevel switching, in-memory computation scheme (MAC or bit-wise) as well as integration location (in-pixel, near pixel or memory) there is still a wide range of different applications yet to be explored. In this work, RRAM analog switching properties of based on different materials stacks, inference-like applications such as error correcting code implementation, genome alignment, and in-sensor AI inference acceleration have been investigated.

Fibrosis, characterized by aberrant deposition of extracellular matrix (ECM) is a contributor to about 45% of deaths worldwide. Fibrosis in the salivary gland, caused by Sjogren’s syndrome, diabetes mellitus, or radiation therapy for head and neck cancers, results in salivary hypofunction characterized by reduced saliva output or changes in its composition, leading to poor oral and digestive health. Current therapies for salivary hypofunction are palliative and inefficient, and regenerative strategies are an appealing therapeutic alternative. Mesenchymal stem/stromal cell (MSC) therapy can limit fibrosis but faces translational challenges due to transient therapeutic effects. Scaffold-based approaches can improve the efficacy of MSC delivery by localizing MSCs near the tissue, improving MSC engraftment and persistence, potentially modulating the in vivo tissue-resident cells, and promoting tissue regeneration. Ideally, scaffolds should emulate native soft tissue ECM to provide key physical, biochemical and mechanical cues that maintain the regenerative potential of MSCs. In this work, we address the limitations of current scaffolding technologies, by developing a novel cryoelectrospinning process, and exploring scaffold chemistry to fabricate scaffolds that mimic the minimal fibrous backbone, porous morphology, and viscoelasticity of decellularized salivary glands (DSG). We used elastin and alginate as natural, compliant biomaterials and water as the solvent for cryoelectrospinning biocompatible scaffolds. We optimized process parameters to produce a unique honeycomb topography, similar to DSG, and optimized collector plate geometries to produce a high throughput yield of >100 scaffolds/run. We demonstrated 3D stromal and epithelial growth on our cryoelectrospun scaffolds (CES) and showed that their coculture facilitated cell-cell interactions resembling normal tissue structure. We demonstrated the feasibility of maintaining MSC-like primary embryonic day 16 (E16) mesenchyme on CES and the ability of CES to repress fibrotic activity, similar to DSG. We also determined that FGF2 supplementation improved stromal health of primary embryonic mesenchyme on CES. Finally, we demonstrated the antifibrotic properties of CES, primary E16 mesenchyme, and FGF2 by the repression of fibrotic activity of myofibroblasts. Overall, in this work, we have developed novel scaffolds that mimic soft tissue ECM and show great potential for use in in vitro organ models and stromal cell delivery for in vivo regenerative therapy.

Current trends towards more efficient computing include alternatives to the standard von Neumann architecture and a trend towards in-memory computing with non-volatile memory (NVM) arrays. Resistive Random-Access Memory (ReRAM) is a strong NVM candidate for such applications. In this work, performance optimization and reliability studies were performed using hafnium oxide and tantalum oxide-based ReRAM devices integrated into 65nm CMOS. A thorough analysis of the effects of switching parameters such as maximum current, voltage, pulse width, and temperature-based stress culminated in improved device performance, yielding a memory window (MW) > 30, excellent endurance >2.1x1010, and retention at multiple resistance levels for 104 seconds without degradation. When the operational temperature was ramped from 25-125 °C oxygen vacancy mobility and generation rates shifted in these devices, directly affecting MW by up to 2X. To demonstrate the potential of ReRAM for neuromorphic applications, multilevel (analog) switching was implemented, achieving a total of >10 statistically distinct resistance levels when using large (>50 ns) pulses. When using ultra-short pulses (300 ps) the number of resistance states was limited to < 15 and resulted in a narrow conduction window (CW) of ~2X. Thus, an optimized pulsing scheme, incremental pulsing (ISPP), was utilized in which successive switching pulses increase in voltage amplitude. When used in conjunction with a read-verify scheme, the total number of resistance states increased to >20 and the CW increased to ~31X respectively, while also maintaining the linearity and symmetry of potentiation or depression. Based on these empirical data, the Neural Network (NN) learning algorithm “Cross-Sim” simulator was trained on the MNIST dataset, yielding 96.55% accuracy, on a 96.7% baseline, when using the ISPP algorithm. Taken together, these results demonstrate the potential of ReRAM 24 for non-von Neumann computing applications once proper optimization of electrical switching parameters and operational temperature is achieved.

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.