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Small-to-big physics : an engineering physics model for broadening participation in nuclear science and engineering
No full textAugust 2022School of EngineeringThe human brain is a byproduct of the natural process of evolution, as is the knowledge it contains. Like other natural resources fit for human consumption, knowledge can be modeled as a physical entity that can be sought after and acquired. In ancient times, knowledge was geographically constrained as a consequence of being a physical entity located inside the mind of an individual or in another physical form that could be transferred. These geographic constraints informed how education processes would evolve to satisfy relevant societal needs. The emergence of telecommunications technology in the second half of the 20th century have persistently eroded the legacy geographic constraints that have limited access to knowledge. The internet allows individual humans to access a seemingly infinite amount of information from nearly everywhere on the planet almost instantaneously. This unprecedented access disrupts legacy barriers to knowledge in the form of financial, cultural, or regulatory restrictions. Education processes optimized when access to knowledge was geographically constrained seem ill-suited for a modern ecosystem with ubiquitous access to global knowledge.
Many of the systems used in the global telecommunications network that serve a critical role in modern society have been developed using principles of modern physics. Unfortunately, the pre-college system used to educate the general public for labor force considerations have not yet incorporated modern physics concepts due to the clandestine nature in which early modern physics-based technology was developed. The gap between widespread utilization of these technologies and the lack of sufficient labor force training has created the need for a novel education methodology that can teach certain STEM fundamentals efficiently and effectively.
A novel framework for teaching modern physics fundamentals, referred to as Small-To-Big Physics has been developed. This novel framework is a combination of cognitive psychology theories coupled with a novel sequence of scientific theory introduction. The cognitive psychology portion of the framework informs how various scientific theories should be presented to students for the purpose of concept mastery. The scientific theory portion of the framework contains the minimally necessary concepts that will enable students to rapidly assimilate a broad range of STEM concepts in preparation for subsequent STEM careers.
The Small-To-Big Physics Framework was developed in various military, private sector engineering, pre-college, and college educational environments. To the maximum extent practicable, cognitive psychology theories were tested in real-world learning scenarios. Where appropriate, empirical guidelines were developed to inform educational methodologies in instances where existing theories were not able to achieve desired outcomes.
Experimental curricula developed using the Small-To-Big Physics Framework has been used in elementary and high school classrooms in New York and Connecticut respectively with overwhelmingly positive results. The feedback from those sessions has been useful in developing a long-term validation plan to incorporate Small-To-Big Physics methodologies into the existing education infrastructure. Several options for exploratory scientific research, identified using the Small-To-Big Physics methodology, have also been included for additional consideration.DEn
Knowledge Graph Construction from Data, Data Dictionaries, and Codebooks: the National Health and Nutrition Examination Surveys Use Case
No full textCDC’s National Health and Nutrition Examination Surveys (NHANES) is a continuous survey that aims to study the relationship between diet, nutrition, and health and their roles in designated population subgroups with selected diseases and risk factors. Data is acquired using questionnaires (either by human interviewers or computer-assisted), aimed at collecting data about participants’ households and families, medical conditions, substance usage, and more. NHANES data and supporting documentation, including data dictionaries (DDs) and codebooks (CBs), are made publicly available and are used in many data science efforts to support a wide range of health informatics projects. A typical use of NHANES data requires a complex human interpretation of the data with the help of the DDs and CBs. For example, to retrieve “diseases treated by a specific drug in households with annual income under $20,000”, one should select all the relevant variables (diseases, drugs, household income, participants) across the relevant datasets (demographic, drug usage) and perform a series of transformations (normalizing disease and income codes) to generate the answer for the query. During data processing, it is not uncommon for data to be misinterpreted as NHANES may use the same variable for multiple purposes (e.g. the same variable is used for diseases being treated and diseases being prevented by a drug and sometimes this distinction is critical to applications). Furthermore, the result of this processing may be incorrectly combined (e.g., harmonized with new data, from NHANES or other studies).
We present our approach for translating NHANES’ datasets, metadata, and any additional documentation from the surveys into a rich knowledge graph (KG) that maintains semantic distinctions. We leverage the Human-Aware Data Acquisition Infrastructure (HADatAc) [1] and its underlying Human-Aware Science Ontology (HAScO) [2], to systematically represent the complete data acquisition process. Semantic Data Dictionaries (SDDs) [3], which are derived from DDs and CBs, support the elicitation of objects that are not directly represented within NHANES datasets (including household, the household reference person, drug usage for disease treatment, drug usage for disease prevention, etc.). We demonstrate1 how we use the KG to generate tailored datasets based on user choice of variables and alignment criteria across multiple NHANES datasets. Our use of SDDs enables the combined use of ontologies and data. We further demonstrate that once data is encoded into the KG, the KG can be used to support complex automated data harmonization that until now, when required in any kind of meta-analysis study based on NHANES, is still done manually
Practicing plant-human solidarity : critical ecosocial art, phytocentric pedagogy, and the lawn (re)disturbance laboratory
No full textDecember 2021School of Humanities, Arts, and Social SciencesUsing a plant-led methodology guided by vegetal beings commonly known as weeds, in this dissertation I provide an artistic and scholarly analysis of a field of artistic practice I am defining as critical ecosocial art (CEA). Indebted to but distinct from related fields of interdisciplinary artistic practice, CEA revolves around hands-on fieldwork, from public un-lawning sculptures to feral watercolor experiments, combining socially engaged art and ecological art through the lens of multispecies studies. With fields like urban ecology, critical plant studies, land-based pedagogies, and feminist and Indigenous science studies as key interlocutors, I outline the influences, conceptual frameworks, and methodologies that are central to this form of artistic practice. Particularly important to me are forms of CEA that cultivate plant-human solidarity in relationship with disturbed habitats impacted by resource extraction associated with capitalism, colonialism, and industrialization. I argue that building interspecies allyship in these settings has the potential to contribute in specific and specialized ways to the intertwined struggles to dismantle exclusionary forms of human supremacy and cultivate ecosocial justice, essential tasks for those of us who find ourselves alive in—and complicit with—the era now contestedly known as the Anthropocene. Through an analysis of my ongoing collaborative project the Lawn (Re)Disturbance Laboratory, I demonstrate how phytocentric pedagogy—one way of doing critical ecosocial art—provides a bundle of intertwined artistic strategies for teaching and learning with and from the weedy plants who dwell alongside us on damaged land. With these tools I join many others working to rebuild plant-human solidarity in a multitude of ways that resist extractive, commodification-based relationships to vegetal life and strive towards mutually beneficial forms of plant-human-land relationships that enable multispecies thriving for as many kinds of beings and lifeways as possible.Ph
Numerical study of mechanics of phonation
No full textMay 2022School of EngineeringHuman phonation is a highly complex process that involves three-way interactions between fluid dynamics of the glottal flow, acoustic waves in the vocal tract, and structural mechanics of the vocal folds. Due to its complex nature, the underlying phonation mechanism is yet to be fully understood. Available clinical measurements are often insufficient in diagnosing the pathology of voice disorders and the severity of them. Since in vivo measurements of glottal flows are difficult due to the space limitations of the larynx, numerical simulations and experiments using scaled-up models are often used to study the behaviors of vocal folds, glottal flows, and sound generation/propagation. There have been a variety of numerical studies on the coupled system. However, so far, there are limited studies that aim at a quantitative description of the overall process using a fully-coupled aeroelastic-aeroacoustic approach. To accomplish this, two major challenges must be overcome: 1. Appropriate models should be employed to resolve the coupled system that requires both fluid-structure interaction and aeroacoustics. A suitable FSI scheme is needed to identify the interface accurately to capture the wave motion of the vocal fold surface, i.e., mucosal waves, especially at the glottal gap which can be very small when the glottis is closing. The aeroacoustic model should be able to resolve both fluid dynamics and acoustics. 2. It is difficult to quantify the acoustic and energy efficiency in the phonation process since the contributions of each part in the aeroacoustic sources and the energy budget are not directly measurable and their relationships remain unclear. Therefore, an effective tool is desired to provide a unified quantification of each contribution and reveal the physical significance and the underlying mechanism of phonation. To address the above issues, first, a coupled fluid-structure-acoustic finite element algorithm is developed based on the immersed finite element method (IFEM). The algorithm is enhanced in several aspects to overcome the numerical difficulties mentioned in regards to accurate modeling of fluid-structure-acoustics. A slightly-compressible fluid model is developed to capture the nonlinear coupling of aeroacoustics. Spalart-Allmaras turbulence model is implemented to resolve subgrid-scale vortices in the glottal jets. Non-reflecting boundary conditions using the Perfectly Matched Layers (PML) are strategically applied to eliminate the spurious reflection of acoustic waves that would otherwise appear on non-treated numerical boundaries. A sharp FSI interface algorithm is developed and employed to accommodate the vocal fold surface waves. Additionally, A grid study in both temporal and spatial resolution is done to study their impact on the solution. To characterize the aeroacoustic source strengths and phonatory flow, a control volume framework is developed to quantify the contributions of each part of the aeroacoustic sources as well as the energy budget in terms of volume integrals in the larynx region. Using the control volume approach, the significance of all the quantities present in the aeroacoustic sources and mechanical energy balance equations can be directly compared to each other. This control volume analysis is performed on-the-fly in the numerical simulation of the coupled system. This research can help us understand the intrinsic physics of the complex behavior behind human phonation, which may in the future help develop novel diagnostic measurements of voice disorder.Ph
Exploration of communication interconnection network congestion and methods of mitigation through simulation
No full textAugust 2021School of ScienceWhen designing the architecture for a supercomputer, there are many facets of design to consider. Among them, and possibly most important, is the choice of communication network that interconnects the thousands of processors together. The selected communication network forms the backbone of the system, allowing for massive scale parallelism and inter-process coordination. Different patterns of interconnection, or network topologies, have different strengths and weaknesses. The cost of building a supercomputer is often a significant factor that influences the choice of network topology. Prospective system builders aim to get the highest level of expected performance given their budget and poor or ill-informed choices in system design can be very costly mistakes. Thus, having reliable predictions of how different communication network topologies behave is a critical step in system acquisition. Simulation allows for the rapid testing and procurement of expected performance metrics of full-scale networked systems without needing to physically build them or compromise testing scale. Network topologies connect switches and any attached compute nodes to each other forming paths of communication from one endpoint to another. As compute nodes inject traffic into the network, packets containing the contents of communication will be routed from one switch to another until finally reaching their destination. With increased levels of traffic, switches may become overburdened, receiving more packets at a rate faster than what they are able to process and route. This imbalance will mean that any packets traversing the overloaded switch will become delayed as they sit in a queue in the switch's memory waiting to be routed. A switch becoming overloaded is a point of local congestion. Eventually, if the situation remains unresolved, the buffer space on the switch will become full and cannot receive any more packets until another already in its memory is routed away. If other switches have packets destined for the overloaded and full switch, then they may find themselves waiting to forward packets and, consequently, their buffer space begins to fill up. The local congestion previously found on a single switch begins to spread to other nearby switches and the problem worsens, interfering with many more packets and resulting in poor application performance. Network topologies can be designed to be more resilient to the effects of network congestion. For example, having a high diversity of possible paths between any two endpoints can provide more alternative routes for packets should one become congested. Clever routing schemes to more effectively balance load across the network or to route around observed points of local congestion can work to mitigate the effects of congestion and thus minimize packet interference. In this work, I look to study, through effective simulation, situations for the occurrence of congestion as well as technologies and methods to mitigate and resolve it. This document provides an overview of various methods for the avoidance of congestion, including the usage of adaptive routing, quality-of-service techniques, and network topology design. Additionally, it also explores two techniques for the mitigation and treatment of congestion through detection, causal identification, and abatement strategies. Lastly, this document proposes new techniques for effectively simulating large parallel discrete event simulations with simultaneous events -- such as network simulation -- and demonstrates how it can be used to gain deeper insight into the characteristics of simulated models.Ph
Integer optimization for the understanding and disruption of illicit networks
No full textAugust 2022School of ScienceTools from operations research (OR) have historically been applied to better understand and disrupt illicit trafficking networks. Two such tools are community detection and network interdiction. Community detection has been used to aid in understanding the roles of participants of illicit networks. However, edges may be hidden in an attempt to prevent correct classifications of participants in the network. Network interdiction models have been applied to disrupting illicit networks, such as drug smuggling and nuclear smuggling networks. Current network interdiction models do not allow for the networks to change after disruption decisions have been implemented, which is contrary to how we expect these networks to behave. This is especially important since we will be applying network interdiction models to disrupting human trafficking networks. There are many opportunities for the OR community to help address human trafficking. However, there are many challenges associated with applying OR tools to these networks. One of the major challenges associated with applying operations research models to disrupting human trafficking networks is a limited amount of reliable data sources readily available for public use, since operations are intentionally hidden to prevent detection, and data from known operations are often incomplete. This dissertation considers new integer optimization problems to better understand and disrupt illicit trafficking networks. We first consider two new problems regarding how edge addition or removal impacts the modularity of partitions (or community structures) in a network. The first problem seeks to add edges to enforce that desired partitions are the the partitions with maximum modularity. The second problem seeks to find the sparsest representation of a network that has the same partition with maximum modularity as the original network. We present integer programming formulations of these problems, as well as heuristic algorithms to solve them. We then consider a new class of max flow network interdiction problems, where the defender is able to introduce new arcs to the network after the attacker has made their interdiction decisions. We provide an example of when interdiction can result in an increase to the maximum flow, and prove properties of when this restructuring will not increase the value of the minimum cut, which is known to be equivalent to the maximum flow. This has important practical interpretations for problems of disrupting drug or human trafficking networks. In particular, it demonstrates that disrupting lower levels of these networks will not impact their operations when replacing the disrupted participants is easy. For the bilevel mixed integer linear programming formulation of this problem, we devise a column-and-constraint generation (C&CG) algorithm to solve it. Our approach uses partial information on the feasibility of restructuring plans and is shown to be orders of magnitude faster than previous C&CG methods. We then extend this problem to include a temporal component, where interdictions and restructurings are implemented throughout the time horizon. Modeling adaptations are proposed to reduce the size of the problem, and modeling-based augmentations are implemented in the proposed C&CG algorithm to further improve the solve time of our method. To apply our models to the disruption of sex trafficking networks, we propose a novel conceptualization of flow as the ability of a trafficker to control their victims. Our results show the importance of understanding how sex traffickers react to disruptions, especially in terms of recruiting new victims. Additionally, we help address the data problem in human trafficking by proposing a network generator for domestic sex trafficking networks by integrating OR concepts and qualitative research. Multiple sources have been triangulated to ensure that networks produced by the generator are realistic, including law enforcement case file analysis, interviews with domain experts, and a survivor-centered advisory group with first-hand knowledge of sex trafficking. The output models the relationships between traffickers, so-called ``bottoms", and victims. This generator allows operations researchers to access realistic sex trafficking network structures in a responsible manner that does not disclose identifiable details of the people involved. We demonstrate how the networks produced by the network generator can be used as data for the network interdiction models.Ph
Structure-property relationships in poly(pro-17-estradiol) materials and the effect on in vitro degradation kinetics
No full textAugust 2021School of EngineeringDue to the refractory nature of the central nervous system (CNS), injuries such as spinal cord injury (SCI) trigger a complex injury cascade. Currently, no effective therapeutics exist to reverse the outcomes of progressive secondary injuries caused by SCI. The debilitating, long-term effects of SCI have created the need for a controlled extended-release drug delivery platform. 17-Estradiol (E2) is known to have neuroprotective, neurotrophic, and immunosuppressive properties making it a promising therapeutic for CNS injuries. The central motivation of this work is to examine structure-property relationships in polymerized estrogen materials to create tunable material properties and release kinetics. The design, testing, and characterization of poly(pro-E2) biomaterial scaffolds is reported and presents a promising drug delivery platform to target CNS injuries. The strategies employed in the synthesis of the library of poly(ester) and poly(carbonate) E2 prodrugs with varying chain modifiers presented is highly applicable to other polymer classes leaving limitless possibilities to tailor material properties and control drug release kinetics.M
NECE: Narrative Event Chain Extraction Toolkit
No full textTo understand a narrative, it is essential to comprehend its main characters and the associated major events; however, this can be challenging with lengthy and unstructured narrative texts. To address this, we introduce NECE, an open-access, document-level toolkit that automatically extracts and aligns narrative events in the temporal order of their occurrence using sliding window method. Through extensive human evaluations, we have confirmed the high quality of the NECE toolkit, and external validation has demonstrated its potential for application in downstream tasks such as question answering and bias analysis. The NECE toolkit includes both a Python library and a user-friendly web interface; the latter offers custom visualizations of event chains and easy navigation between graphics and text to improve reading efficiency and experience
Studies of manganese-based electrocatalysts for oxygen reactions
No full textAugust 2020School of EngineeringBifunctional catalysts capable of catalyzing both oxygen reduction (ORR) and oxygen evolution (OER) reactions are extremely valuable for oxygen-based energy conversion devices such as regenerative fuel cells and metal-air batteries. However, the underlying property of such catalysts that gives rise to their bifunctionality is not yet known nor explored. With the first use of near infrared photoluminescence spectroscopy for tracking the changes in the individual metal cation valence states during electrocatalysis in combination with in-situ gravimetric and resistance measurements, we show the underlying correlation between catalytic activity, potential-dependent resistance and nature of reaction intermediates on various bifunctional and non-bifunctional surfaces. Our results show that bifunctional Mn2O3 reversibly switches electrical polarity from p-type to n-type along with the formation of high-valent cationic Mn4+ active sites as well as low-valent cationic Mn2+ active sites during OER and ORR, respectively, which is absent in non-bifunctional NiO and Co3O4. Results also show other key process steps such as lattice hydration/dehydration other occur during polarization. These results are rationalized in terms of a band structure framework that correlates electrochemical activity with the formation energy of various metal cation intermediates. In the second study, we correlate electronic phase transitions, formation of reaction intermediates, and gravimetric results to the mechanism by which oxygen reactions occur on different crystal structures of MnO2. OER that occurs via adsorbate evolution mechanism (AEM) involves the formation of higher oxidation states that stabilize higher order reaction intermediates, whereas OER that occurs via lattice oxygen mechanism (LOM) involves the abstraction of oxygen from the lattice. Despite having the same composition, ????-MnO2 and ????-MnO2 have different activities for OER, which we report is due to their differing electronic structures with respect to the oxygen redox couple. We show that the formation of higher oxidation states (Mn4+, Mn5+), Mn-OH reaction intermediates, and electronic phase transition in the form of n-type to p-type switching occurs in . ????-MnO2 confirming AEM-OER. Moreover, we report consumption of lower oxidation states (Mn2+) and inability to switch type of electronic conductivity in ????-MnO2 and attribute these results to LOM-OER. In the third study, we use interlayer constraining in order to determine the key active sites of ORR in ????-MnO2. Since ????-MnO2 has a layered structure, the intercalation of cations from either LiOH or KOH electrolyte affect the ORR activity, as indicated by Tafel polarization measurements. Furthermore, the electrocatalytic activity was correlated with changes in potential-dependent resistance as well as changes in key Mn oxidation states. Most importantly, the electrode showed increases in resistance during ORR during K+ intercalation, whereas the resistance did not change appreciably during Li+ intercalation. In addition, we show a higher formation of Mn2+ species in ????-MnO2 in LiOH than in KOH. This suggests that Mn2+ is a key ORR active site, which has also been shown in the literature. While the electrode in both electrolytes show formation of Mn2+, the retention of these active sites aided by interlayer constraining as a result of Li+ intercalation may explain the increased ORR activity in LiOH. We propose an interlayer constraining model that has yet to be corroborated with further experiments, along with an electronic band framework in order to explain the differences in activity. The goal of this thesis is create structure-defect-property correlations between manganese-based electrocatalysts to ultimately be used in energy conversion and energy storage applications.Ph
Electron scattering at transition metal surfaces and grain boundaries
No full textJuly 2022School of EngineeringElectron transport in the mesoscopic regime is of great interest to the academic community and the semiconductor industry as a fundamental understanding of conduction in narrow wires is essential for the continued downscaling of integrated circuits and the development of interconnects for nanoelectronic devices and alternative computing approaches. As the interconnect line widths approach the electron-phonon scattering mean free path λ ~ 39 nm in Cu, the resistivity increases due to electrons scattering at surfaces and grain boundaries. Consequently, much of the recent literature has explored alternate metal solutions to alleviate this resistivity bottleneck in Cu by either (1) facilitating specular surface scattering, (2) high electron transmission at grain boundaries, or (3) a small electron phonon scattering mean free path which mitigates resistivity scaling. Despite the large industrial interest, direct measurements that aid to quantify and understand the fundamental physics describing electron surface and grain boundary scattering are absent and, consequently, resistivity scaling in alternate metals remains to be investigated. Thus, the goal of this thesis is to quantify (1) the electron-phonon scattering mean free path λ, (2) the surface specularity parameter p, and (3) the grain boundary reflection coefficient R for the two most promising transition metals Rh and Ir by studying the thickness dependent resistivity of epitaxial and polycrystalline metallic thin films. Furthermore, I investigate the effect of the addition of adlayers and adatoms on electron surface scattering for several epitaxial metals which reveals the breakdown of the semi-classical FS model and a need for a new 2D transport mechanism to explain the observed resistivity increase.In situ and low-temperature transport measurements on single-crystal Rh(001) layers deposited on MgO(001) substrates show a resistivity increase that is well described with the FS model and diffuse surface scattering, yielding an effective room-temperature mean free path eff = 9.5 0.8 nm and a temperature-independent product ρoλ = (4.5 0.4) ×10 16 Ωm2. Two-domain polycrystalline Rh(111) layers deposited on Al2O3("11" "2" ̅"0" ) have resistivity values higher than for the Rh(001) layers, which is attributed to electron scattering at the domain walls, indicate an electron reflection probability R = 0.16 ± 0.03. This value is for grain boundaries characterized by a 60° rotation about the axis and matches the previously predicted R for 3 twin boundaries. The four times smaller λ for Rh in comparison to Cu suggests a much-reduced resistivity scaling which makes Rh a promising alternate interconnect metal.
Secondly, epitaxial Ir(001) layers are sputter deposited on MgO(001) at 1000 °C. In situ and ex situ transport measurements at 295 and 77 K yield an effective electron mean free path λeff = 7.4 ± 1.2 nm and a temperature independent product ρoλeff = (3.8 ± 0.6)×10 16 Ωm2 which is in good agreement with first-principles predictions. However, dewetting is observed for layers with d 10 μm wide for d = 10 nm, resulting in a 26% lower ρoλeff. Ir(111)/Al2O3(0001) layers exhibit two 60°-rotated epitaxial domains with an average lateral grain size of 88 nm. The grain boundaries cause a thickness-independent resistivity contribution ρgb = 0.86 ± 0.19, indicating an electron reflection coefficient R = 0.52 ± 0.02 for this boundary characterized by a 60° rotation about the axis. The Ir microstructure and surface morphology is strongly affected by deposition conditions and layer thickness, making direct quantification of the resistivity size effect challenging. Nevertheless, the measured ρoλeff for Ir is smaller than for any other elemental metal, and 69%, 43%, 25%, and 15% below reported ρoλ products for Co, Cu, Ru, and Rh, indicating that Ir is a promising alternate metal for narrow high-conductivity interconnects.
In order to determine the effect of adlayers and adatoms on electron surface scattering, in situ transport measurements are performed on 10-nm-thick epitaxial metal layers during (1) the addition of Ti capping layers and (b) the exposure to oxygen. Epitaxial Cu(001) and Co(0001) layers exhibit a considerable resistance increase, but a five times smaller effect for Rh(001) on the addition of Ti adlayers and O2 exposure, suggesting a pronounced dependence on the conductor metal but no qualitative difference when changing the chemical identity of the capping layer. Furthermore, measurements on epitaxial layers of many different metals indicate a strong negative correlation between the metal electronegativity and the relative resistance increase Δρ/ρ during air exposure, suggesting that the magnitude of the surface potential perturbation due to charge transfer from metal to adatom is the primary parameter affecting electron surface scattering in thin metal layers. Thus, electronegative metals facilitate smooth surface potentials with specular electron reflection and a minimized resistance increase. They are therefore promising as conductors for highly scaled interconnect lines.
Electron scattering at grain boundaries is investigated using polycrystalline Rh thin films. For this purpose, Rh layers with thickness d = 9 – 261 nm are deposited on SiO2/Si(001) substrates at Ts = 20 °C, 350 °C, and 350 °C followed by in situ stepwise annealing to 750 °C to yield three series of 111-textured layers with increasing average lateral grain sizes D. Electron backscatter diffraction maps show that D for annealed layers increases with layer thickness from D = 89–134 nm, matching the surface morphological lateral correlation length = 86 – 154 nm measured by atomic force microscopy. The thickness and grain size dependence of the resistivity of polycrystalline layers is well described by the combined FS and MS model and indicates a Rh electron mean free path = 9.5 0.8 nm and a reflection coefficient R = 0.41 ± 0.05 for grain boundaries characterized by a rotation about the axis. As-deposited layers with Ts = 350 °C and 20 °C have considerably smaller grains, leading to a 1.8- and 3.5-times higher resistivity than annealed Rh layers for d = 10 nm, respectively. The overall results reveal that a large (>10 nm) grain size is essential to realize the conductivity advantage of Rh vs Cu for narrow interconnect lines.
Finally, a combined experimental and first-principles study is performed to determine the most conductive metal among Cu, Co, Ru, Rh, and Ir in the limit of narrow interconnect lines. Transport measurements indicate that Rh and Ir are promising because their ρo×λ product is the smallest. However, their grain boundary reflection probability is 1.4- and 1.7-times larger than for Cu, negating some of the conductivity benefits of the small λ. Ru is also promising, because of its small ρo×λ value that is 24% below that of Cu but more importantly because of the smaller liner thickness which provides a larger cross-sectional area for narrow Ru interconnect lines.
In summary, this thesis provides original discoveries and new knowledge on the growth and characterization of epitaxial and polycrystalline Rh and Ir layers. More importantly, it contributes to a more complete understanding of electron scattering at surfaces, interfaces, and grain boundaries of Rh and Ir.Ph