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  • Development and Fabrication of Low Voltage (600 V) to High Voltage (15 kV) 4H-Silicon Carbide (SiC) Power Devices

    Yun, Nick (Nung Jun) (2022-12)
    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.
  • The Photovoltaic Properties of Carbon Nanotube Network p-n Diodes

    Oyibo, Gideon (2022-11)
    Single Walled Carbon nanotubes (SWCNTs) are quasi one dimensional rolled up sheets of graphene with amazing optical and electronic properties. Depending on their diameter and roll up angle, SWCNTs come in varying chiralities with multiple bandgaps giving them exceptional properties that make them attractive for photovoltaic applications. One of such properties is the absorption of light across the broad solar spectrum, a highly desirable property in semiconducting solar cell absorbers. In this dissertation, we will be exploring our attempt to fabricate a fundamental device that enables us harness the full sunlight potential of semiconducting SWCNT (s-SWCNT) networks and have a better understanding of its photovoltaic properties. To fabricate this fundamental device, we look to nature for inspiration on solar energy conversion. We use the process of photosynthesis as a model for building our solar energy conversion device. Nature, through centuries of evolution, has perfected the harvesting of light for energy conversion through the process of photosynthesis by employing two main mechanisms carried out by distinct proteins: excitation energy transfer, where light harvesting complexes capture light from multiple regions of the solar spectrum and funnel photoexcitations to a reaction center, and charge separation, where the photoexcitations become free charges in the reaction center. As we will see in this dissertation, SWCNTs have similar properties to that of photosynthetic systems, one of which is the varying chiralities of SWCNTs with different diameters, analogous to the distinct proteins in photosynthetic systems absorbing light at different wavelengths. We fabricate p-n diodes on various networks of s-SWCNTs, we study the intrinsic electronic and optical properties of nearly monochiral and polychiral s-SWCNT networks and form a fundamental understanding of the best s-SWCNT films required to make more ideal diodes. We examine the current-voltage characteristics of these diodes in the dark and find correlations between the key figure of merits, including the diode leakage current and the ideality factor, to different s-SWCNT networks. We also examine their optical properties by measuring wavelength-dependent photocurrent spectroscopy to gain insights into the dynamics of excitons in a network of s-SWCNTs. We achieve ideal diodes, for the first time in a homogenous network of s-SWCNTs. We discuss the limitations of using ideal diodes in the measurement of the electronic bandgap of s-SWCNT networks and then use non – ideal diodes to measure the electronic bandgaps of the s-SWCNT networks for the first time. After a more in-depth understanding of the dark diode characteristics of the s-SWCNT networks, we progress to fabricating a fundamental solar energy conversion device, modelled after photosynthesis. We fabricate photovoltaic diodes mimicking photosynthetic systems. Using different s-SWCNT chiralities, we create an energy funnel in our diodes by layering different s-SWCNT networks according to their bandgaps. The photo excitations in the larger bandgap s-SWCNTs are funneled down to the smallest bandgap s-SWCNT, allowing us to increase the spectral response of our diodes. We show that the photocurrent generation in our energy funnel is more efficient than in diodes formed using single chirality s-SWCNT networks. Finally, we show that our device architecture increases the photocurrent without increasing the highly undesirable dark leakage current. Using the analogy to photosynthetic systems, we use the smallest bandgap s-SWCNT network to create the diode (Reaction Center). The larger bandgap s-SWCNT networks act as light harvesters. We demonstrate an increase in short circuit current and the open circuit voltage as we add these nanotubes sequentially. We use this device to implement the mechanisms of exciton energy transfer in our p-n diodes and study its properties as it applies to s-SWCNT networks. We see some new and exciting physics which we will cover in this dissertation.
  • Regulation of PTP1B Activity and Insulin Resistance by Cellular Cholesterol

    Sagabala, Reddy Sudheer (2022-08)
    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.
  • Resist and Process Pattern Variations in Advanced Node Semiconductor Device Fabrication

    Chih-Fang Liu, Eric (2022-06)
    Pattern variations can cause challenges in device scaling. Since the last few decades, the semiconductor industry has successfully utilized the device scaling technique by reducing the transistor area to meet the requirements needed for optimum device performance and fabrication cost during each generation of development. The main challenges in the development of this technique are imaging resolution and pattern variations. Extreme ultraviolet (EUV) lithography and the multiple-patterning method can be used to push the imaging resolution to sub-30 nm. This thesis investigates the mechanism of pattern variations and proposes methods for pattern improvement. The thesis begins by investigating the origin of pattern variations in an EUV–chemically amplified photoresist system. The experimental results show that the chemical composition and inhomogeneity of the material contribute to pattern variations in EUV lithography. A difference in the localized-material-removal rate indicates the contribution of stochastics chemical kinetics in the photoresist during the development process. The study then investigates the effects of the plasma etching process on the pattern variations. The plasma etching process can alter the pattern variations by modifying the etching behavior and the etching selectivity. The thesis also discusses the system-level or integrated process-induced pattern variations. The method proposed herein involves surface modification and tone inversion technique and reduces the line edge roughness by 26% on a 20-nm pitch line pattern. Using a multicolor line-cut process, the thesis experimentally demonstrated the control of the edge-placement error from system-level pattern variations.
  • Microfluidic Imaging Windows for Study of the Tumor Microenvironment

    Head, Tristen (2022-08)
    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.
  • Therapeutic Targeting of Oncogenic Gain-of-Function Mutant p53 by Proteasome Inhibition

    Oduah, Eziafa I. (2022-07)
    Non-small cell lung cancer (NSCLC) is a molecularly complex and heterogenous disease. Recent advances in genomic profiling have changed the therapeutic landscape of NSCLC to incorporate targeted and immunotherapeutic approaches. Despite these advances, lung cancer remains the leading cause of cancer mortality in the United States and worldwide. This is partly because these novel treatments are not applicable to all patients and are often associated with primary or secondary resistance. This highlights the need for continued search for new therapeutic agents and strategies for NSCLC patients. However, the drug discovery and development pipeline is protracted and inherently expensive for new drugs. The projected timeline from identification of a new drug candidate from preclinical research to clinical trials and approval is estimated at about 12-15 years with an average cost of $1.3 billion [1]. Moreover, the failure rate for new drugs during the clinical development stage is high, reaching up to 96% by some estimates [2] and is partly due to adverse risk profiles of candidate molecules. Given the ongoing need for continued drug development in lung cancer, repurposing previously approved drugs for new indications when possible is advantageous. Such strategies decrease the cost and timeframe of drug development and pose a lower safety risk to patients since the toxicity profiles of the repurposed drugs are already well established. Drug repurposing has had success in cancer therapy. Some of these include the repositioning of thalidomide for use in multiple myeloma and the repurposing of rituximab from lymphoma to incorporate its use in rheumatoid arthritis [3]. Interestingly, the observations that led to many drug repurposing efforts were serendipitous by nature. However, recently more systematic approaches to repurposing drugs are being employed and include retrospective clinical analysis, genetic associations and pathway matching, binding assays to identify relevant target interactions, and large-scale in vitro drug screens with paired genomic data [3]. In this thesis compilation, I first and foremost lay the groundwork for repurposing proteasome inhibitors for therapeutic targeting of gain-of-function (GOF) oncogenic mutant p53 using lung cancer as a model disease. This has a potential for generalizability across cancers that bear GOF p53 mutations since alterations in TP53 are central to carcinogenesis and prevalent across tumor types. As the ‘guardian of the genome’, p53 maintains the genome integrity by inducing DNA damage repair or forcing aberrant cells into apoptosis or senescence. Failure of this function results in propagation of abnormal cells and the progression from normal to precancerous and malignant cells. Moreover, gain-of-function (GOF) activities of mutated TP53 related the acquisition of novel oncogenic properties are well described in the literature and are related to excess accumulation of the mutant protein. This work describes the mechanism of paradoxical destabilization of GOF p53 by proteasome inhibition in lung cancer and identifies ‘hyperactive’ proteasome genes in mutant p53 as targetable vulnerabilities in this subset of NSCLC. Since proteasome inhibitors are FDA approved drugs and prior drug candidates targeting p53 have not had success in clinical development, the final goal is to repurpose proteasome inhibitors to target GOF p53 mutant NSCLC.
  • Stock Price Prediction Using Sentiment Analysis and LSTM

    Carter, Caymen (2022-05)
    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.
  • Application of Resistive Random Access Memory (RRAM) For Non-Von Neumann Computing

    Rafiq, Sarah (2022-05)
    The movement of data between physically separated memory and processing units in conventional computing systems (the so-called von Neumann architecture) incurs significant costs in energy and latency. This is known as the von Neumann bottleneck. With the advent of the Internet of Things (IoT) and edge computing, computing systems are also becoming significantly power limited. In this work, hafnium oxide resistive random access memory (ReRAM) integrated with 65nm CMOS technology on a 300 mm wafer platform was assessed to carry out two novel non-von Neumann computing applications that processes data within memory and avoid excessive data movement. These computing applications are based on regulating the flow of sneak path currents in memory arrays to perform computation, called flow-based computing, and detecting degree of association (correlation) between binary processes in an unsupervised manner using the ReRAM non-volatile accumulative behavior, termed as temporal correlation detection. Electrical characterization of hafnium oxide ReRAM arrays was conducted for multi-level resistance states for flow-based computing, which was then investigated for two functions, approximate edge detection and XOR Boolean logic, through both experiments and simulation. The effect of device non-idealities was also evaluated. A trade-off between the flow-based output resistance ratio and the variability of flow-based outputs was found for different patterned binary resistance Roff/Ron ratios. For the second non-von Neumann application, the feasibility of ReRAM as a non-volatile candidate device was investigated with an empirical ReRAM model through simulation. Experimental ReRAM analog incremental switching data, from both SET and RESET regimes, was also evaluated on the modified temporal correlation detection algorithm, where the RESET regime resulted in better performance. The ReRAM based implementation yielded 36,000-53,000 vi times lower energy consumption than similar implementation with phase change memory for 25 binary processes, and a speed-up of computation time by 1,600-2,100 times than that of a CPU-based implementation using 1xPOWER8 CPU. 1xPOWER8 CPU is a CPU available on the IBM* Power* System S822LC system, the POWER8 system series, where the CPU was run for 1 thread. In summary, hafnium oxide ReRAM based on 65nm CMOS technology has been evaluated for two non-von Neumann computing applications, and the effect of device non-idealities has also been assessed. These ReRAM in-memory computing applications show the promising potential of ReRAM in overcoming the von-Neumann bottleneck.
  • sxRNA Switches: Hypothesis Through Automated Design Via a Genetic Algorithm Approach

    Tenenbaum, Scott; Chair; Melendez, J.A.; Cady, Nathaniel; Fasullo, Michael; Begley, Thomas; Doyle, Francis J., II (2021-12)
    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.
  • Development of High-Performance Hafnium Oxide based Non-Volatile Memory Devices on 300mm Wafer Platform for Data Storage and Neuromorphic Applications

    Diebold, Alaine (Committee member); Ventrice, Carl A. Jr. (Committee member); Lloyd, James (Committee member); Kurinec, Santosh (External committee member); Cady, Nathaniel (Dissertation Committee Chair); Hazra, Jubin (2021-08)
    Fundamental limitations associated with scaling and modern von Neumann computing architectures illustrates the need for emerging memory solutions in the semiconductor industry. One such promising non-volatile memory (NVM) solution is resistive random access memory (RRAM), which is seen as a potential candidate that can meet the performance needs of DRAM and the density of NAND Flash in terms of scalability, reliability and switching performance. However, reliable operation of RRAM devices requires further development to remedy device- to-device and cycle-to-cycle uniformity variation, increase the conductance window, and to improve retention, yield and endurance properties. This research work primarily focuses on improving RRAM performance metrics through optimization of processing conditions and programming algorithms for CMOS-integrated nanoscale HfO2 RRAM devices on a full scale 300mm wafer platform. It was observed that tuning of ALD parameters during RRAM switching layer HfO2 deposition had a significant impact on device switching performance. An excellent memory window of >30 with switching yield ~90%, along with low cycle-to-cycle (σ <0.5) and cell-to-cell variability (σ <0.4) were achieved for tested 1 Transistor 1 RRAM (1T1R) cells across full 300mm wafers. The devices demonstrated excellent endurance (>1010 switching cycles) and data retention performance at elevated temperature (105 s at 373K). The fabricated RRAM cells were also optimized for multi-level-cell switching behavior and ~10 distinct resistance levels were obtained through a combined current- and voltage-control based programming approach. An incremental pulse write technique combined with read verification algorithm enabled accurate resistance states programming within a large resistance window along with linear and symmetric potentiation-depression characteristics yielding superior analog synaptic functionality of fabricated RRAM devices. In addition to RRAM devices, hafnium zirconium oxide (HZO) based nanoscale ferroelectric tunnel junction (FTJ) devices were successfully implemented on a 300 mm wafer platform. Current measurement, as a function of voltage for both up and down polarization states, yielded a tunneling electroresistance (TER) ratio of ~5 and switching endurance up to 106 cycles in TiN/ Al2O3/ Hf0.5Zr0.5O2/ TiN FTJ devices distributed across full 300 mm wafer. Investigation of current transport mechanisms showed that the conduction in these FTJ devices is dominated by direct tunneling (DT) at low electric field and by Fowler-Nordheim (F-N) tunneling at high electric field. The realization of CMOS-compatible nanoscale RRAM and FTJ devices on 300mm wafers demonstrates the promising potential of these devices in large scale high-yield NVM manufacturing for high performance embedded memory and mass data storage applications.
  • ULTRATHIN HIGH-K OXIDES FOR AREA-SELECTIVE DEPOSITION AND CHARACTERIZATION BY BALLISTIC ELECTRON EMISSION MICROSCOPY AND X-RAY PHOTOEMISSION SPECTROSCOPY

    Rogers, Jack (2021-05)
    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.
  • Bioengineered Platforms for Human Stem Cell-Based Diagnostic and Therapeutic Interventions

    Paluh, Janet L.; Thesis Advisor; Sharfstein, Susan T.; Committee Member; Xie, Yubing; Committee Member; Wang, Jun; Outside Committee Member; Amini, Nooshin (2020-08)
    Human stem cells offer an unprecedented ability to restore function lost through disease or injury by providing options for cell therapies and regenerative medicine. Two hurdles that delay greater clinical use of stem cells are production of differentiated therapeutic cells in large-scale platforms and the challenge of choosing the optimum cell type and delivery method for cell therapy that is optimized for cell-cell signaling in the therapeutic microenvironment. In my thesis work I investigated different bioengineered platforms in combination with human stem cell technology to mass produce functional hiPSC-derived beta islets in a miniature bioreactor and study cytokine release from multipotent and differentiated hiPSC-derived neural stem cells as neural rosettes and their dissociated cells or differentiating inhibitory and excitatory neurons alone and in mixed cultures applying a neural cell-cell interaction microchip (NCCIM) with features developed specifically for these studies. My work has further expanded the application of hiPSC-derived neurons in an in vitro model of traumatic brain injury. In this study a hybrid culture of hiPSC-derived excitatory pyramidal neurons, inhibitory GABAergic interneurons and immortalized human microglia are being evaluated for secreted cytokines under healthy and stretch injured induced conditions. One of the challenges of TBI is the inability to yet effectively and with minimal invasiveness track changes following injury that may indicate healing or deterioration and an in vitro model is one important contribution to identifying biomarkers.
  • MAXIMIZING THE CHEMICAL REMOVAL OF CERIA ABRASIVES IN CMP FOR SILICON OXIDE AND METAL POLISHING

    Thiel, Brad; Defense Committee; Carpenter, Michael; Defense Committee; Borst, Christopher; Defense Committee; Hatzistergos, Michael; Defense Committee; Dunn, Kathleen; Committee Chair; Netzband, Christopher M. (2020-08)
    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.
  • BIOENGINEERED, STEM CELL DERIVED OCULAR OUTFLOW TISSUE

    Xie, Yubing; Advisor; Torrejon, Karen; Thesis committee; Cady, Nate; Thesis committee; Danias, John; Thesis committee; Sharfstein, Susan; Thesis committee; Tian, Yangzi Isabel (2018-10)
    Glaucoma is one of the leading causes of irreversible blindness in the world. Despite decades of research, intraocular pressure (IOP) is the only known treatable risk factor. IOP is affected by the timely removal of aqueous humor through the conventional outflow track, which is made up of the trabecular meshwork and adjacent Schlemm’s canal. Dysfunction in these tissues due to aging, oxidative stress, metabolic or pathological changes lead to increased flow resistance, elevated IOP, and ultimately glaucoma. Recent advances in ocular regenerative therapy have the potential to rescue glaucomatous tissue function and restore its delicate microenvironment. The possibility of using stem cell-derived trabecular meshwork and Schlemm’s canal cells to recreate a functional outflow tissue are explored in this thesis. Previously, our lab developed a well-defined, micro-porous substrate that promotes in vivo-like physiology and outflow function in primary trabecular meshwork and Schlemm’s canal cell cultures. Using these primary cell cultures as controls, we have created 3D stem cell-derived outflow tissues, evaluated and compared their morphology, expression, outflow facility, and drug responsiveness. To explore the importance of the dynamic microenvironment in outflow function, we developed a dual-flow microfluidic chamber that mimics the basal-to-apical and shear flow of aqueous humor through the conventional outflow track. Overall, this dissertation demonstrates the promising application of stem cells in future glaucoma drug screening and treatment.
  • Mapping, Implementing, and Programming Spiking Neural Networks

    Cady, Nathaniel; Chair; Cafaro, Carlo; LaBella, Vincent; Oktyabrsky, Serge; Plank, James; External Committee Member; Olin-Ammentorp, Wilkie (2019-03)
    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.
  • Assessing a Multi-Electron Beam Application Approach for Semiconductor Process Metrology

    Mukhtar, Maseeh; Thiel, Bradley; Dissertation Committee Chair; Bello, Abner; Dissertation Committee; Diebold, Alain; Dissertation Committee; Cady, Nathan; Dissertation Committee; Geer, Robert; Dissertation Committee; Sung, Woongje; Dissertation Committee (2018)
    Radical and disruptive technological approaches regularly require experimental prototypes be built, which is difficult to justify considering their oft-prohibitive requirements in terms of financial and/or time commitments. It is also frequently the situation that use cases for new technologies are not entirely worked out precisely which in turn make it even more difficult to build prototypes but the analysis of simulation data sets from virtual samples can be used to predict sensitivity to the devised signal, detection limits, and impact of design rules and material sets. The results can thus be used to guide prototype design. The aim of this work is to develop and demonstrate a predictive approach to technology assessment and prototype design. This work will focus on two such disruptive technology concepts: electron beam defect inspection and critical dimension measurement. These two concepts are based on the transfer from conventional process metrology technologies i.e., brightfield inspection and optical critical dimension scatterometry to multi-electron beam approaches. Here, a multi-scale modeling approach is used to simulate data streams nominally generated by virtual tools inspecting virtual wafers. To this end, Java Monte Carlo Simulator for Secondary Electrons (JMONSEL) simulations are used to generate expected imaging responses of chosen test cases of patterns and defects with ability to vary parameters for beam energy, spot size, pixel size, and/or defect material and form factor. The patterns are representative of the design rules for aggressively-scaled FinFET-type designs. With these simulated images and resulting shot noise, a signal-to-noise framework is developed, which relates to defect detection probabilities. Additionally, with this infrastructure the effect of detection chain noise and frequency dependent system response can be made, allowing for targeting of best recipe parameters for multi-electron beam inspection validation experiments. Ultimately, leading to insights into how such parameters will impact tool design, including necessary doses for defect detection and estimations of scanning speeds for achieving high throughput for high-volume manufacturing. Simulated images are also executed for measurement of critical dimensions of the abovementioned class of FinFETs. Similarly, validation experiments for multi-electron critical dimension measurements may use the information extracted for development of volume manufacturing metrology systems.
  • Nanoscale Schottky Barrier Visualization Utilizing Computational Modeling and Ballistic Electron Emission Microscopy

    Nolting, Westly; LaBella, Vincent; Advisor (2018-05)
    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.
  • Characterization and Control of the Surface of the Topological Insulator Bi2Se3

    Green, Avery James; Diebold, Alain; Advisor (2017-12)
    The field of topological insulator (TI) materials is new. The ideal TI contains surface states in helical Dirac cones that can be used for spintronics or interconnect applications. Of the TI class, Bi2Se3 is the most promising for applications due to its stoichiometric composition, its relatively large band gap (0.3 eV), and the central (??-point) position of the Dirac cone in its 2D surface band structure. Although the theoretical solid-state models that the TI field has produced are powerful and unique, their novel emergent physical properties are not universally observed in every sample. These materials are difficult to grow and maintain under ambient conditions. Growths tend to either not be applicable to wafer-scale production or produce high polycrystallinity, and all samples experience natural oxidation, band bending, and intrinsic n-doping, which generates spin-degenerate or bulk conduction. This thesis contains a primer on topologically non-trivial materials, and two studies aimed at understanding and minimizing defects at the surface of Bi2Se3. In the first, the aging process of Bi2Se3 when exposed to air at room temperature is investigated. The time scale and topographic changes of the oxidation process at micromechanically exfoliated surfaces are measured, and an optical model of the bulk and oxide layers are developed. The surface appears to oxidize starting at 2 hours after exfoliation, and continuing through 1.5 weeks, by which time, the oxide layer growth has reached an asymptote of 1.9 nm. New optical characterization methods are developed to monitor the orientation of the crystal (via second harmonic generation) and to measure the oxide growth at the surface (using spectroscopic ellipsometry and the derived dielectric functions of the bulk and oxide layers). The goal of the second study is to assess the use of Se capping and subsequent thermal decapping to preserve a pristine surface and maintain a constant Fermi level. This was measured by annealing samples in a UHV environment to successively higher temperatures until the Bi2Se3 film decomposed, and measuring the surface crystallinity, topography, surface chemistry, and Fermi level between each anneal. Thermally decapping samples has no measurable effect on crystallinity, minimal effect on surface topography, reveals the expected Bi-Se surface bonds, and retains a mid-gap Fermi level. This may serve as a reference to improve the fabrication process of devices that include Bi2Se3.
  • Biomimetic Scaffolds Using Natural/Synthetic Polymers for Salivary Gland Regeneration

    Sfakis, Lauren; Castracane, James; Advisor (2017-06-01)
    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.
  • Development of Novel Technologies for Direct Cellular Patterning for the Establishment of Well Controlled Microenvironments to Facilitate Studies on Cellular Signaling, Sensing, and Other Diffusion-Based Phenomena

    Hynes, William (2016-05-07)
    This work focuses on the utilization of novel bioprinting technologies for the investigation of cellular signaling, sensing, and other diffusion-based phenomena with spatiotemporal dependencies. Two different printing techniques were developed for the purpose of fabricating controlled microenvironments comprised of cells, nutrients, hydrogels, and soluble signaling molecules in a repeatable fashion. The first application explored was the development of a novel, bioprinted, cell-based biosensor as a nondestructive method for the monitoring of the cellular redox environment. Mammalian cells were engineered to express a redox sensitive protein and were patterned and immobilized within a photopolymerizing hydrogel matrix, resulting in biocompatible, three-dimensional microenvironments which supported cell growth and facilitated small molecule sensing. Exposure of the printed, redox sensitive cells to oxidative and reductive compounds and monitoring via confocal microscopy demonstrated proper and reversible functioning of the living biosensor. Bioprinting was also used to generate complex, micro-scale, multi-species populations of bacteria in order to evaluate the effects of distance and various forms of competition on syntrophic relationships. An artificial, syntrophic bacterial consortium was printed within controlled microenvironments confined by geometry and nutrient availability. The growth of the printed strains was monitored, analyzed, and compared to the predictions of an experimental, computational bacterial growth model known as COMETS. Results indicated that the general trends exhibited in vitro by most of the examined micro-scale interactions can be predicted in silico, and that the effects of microbial interactions on the micro-scale can differ considerably than those observed at the macro-scale.