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UC Santa Barbara Electronic Theses and Dissertations

Optimizing Soft and Stretchable Electronics Made with Novel Polymers through Predictive Modeling


The combination of contemporary synthetic chemistry and application-driven materials design accentuates the opportunities available at the intersection of science and engineering to advance soft, flexible, and biocompatible devices. Designing devices which will undergo deformation can be approached in two ways: (1) mitigating deformation to realize strain-tolerant devices that maintain functionality in dynamic environments, e.g., wearable devices that bend and stretch with the body, or (2) leveraging deformation as a device input or output, e.g., strain sensing or actuation. In both cases device performance can be optimized through materials development, guided by electro-mechanical modeling.Devices which leverage deformation have figures of merit that are closely linked to material mechanics. It is of interest to develop novel materials that defy conventional boundaries of mechanical behavior. Bottlebrush elastomers are an emerging class of solid materials that exhibit extremely low elastic moduli. Super-soft bottlebrush elastomer dielectrics and conductive composites have utility in enhancing the performance of flexible devices such as capacitive pressure sensors. Mitigating deformation requires quantitative modeling of strain-dependent device parameters. One particularly interesting device to model is the thin film transistor (TFT), an important building block of modern circuits. Organic TFTs can be made with materials that allow them to be deformed during electrical operation. Mechanical models of the elasticity of polymers can be applied to predict the electrical characteristics of deformable TFTs.

Cover page of Genetic and epigenetic regulation of a core embryonic gene regulatory network

Genetic and epigenetic regulation of a core embryonic gene regulatory network


Development is driven by progressive installation of transcriptional programs and complex interactions between genetic factors that constitute gene regulatory networks (GRNs). How are GRNs structured and deployed to avoid frequent catastrophic failure resulting from environmental and genetic variation? How are GRNs modified during evolution to engender Charles Darwin’s "endless forms most beautiful”? In the nematode C. elegans, the SKN-1/Nrf transcription factor activates a GATA transcription factor-driven cascade that modulates specification and differentiation of the endoderm. We found that six GATA factors (MED-1/2, END-1/3, and ELT-2/7) form a recursive series of interlocked feedforward loops to mediate robust lockdown of endodermal cell fate. We further found that the specification-to-differentiation transition is linked through END-1 that straddles the two processes. Further, we uncovered additional role for key GATA factors as transcriptional repressors in establishing spatial gene expression domains that appear to define the boundaries of the digestive tract (Chapter 2). With the molecular details of the endoderm GRN in hand, we subsequently explored the evolutionary diversification of the regulatory network by exploiting natural genetic variation in the C. elegans wild isotypes. Remarkably, we uncovered extensive intraspecies variation in SKN-1 requirement: some isotypes absolutely require SKN-1 during endoderm development, while in others most embryos differentiate endoderm in its absence. We showed that this polymorphism results from accumulation of cryptic genetic variations, including a missense variant in the transcriptional activator Pur-α homolog, PLP-1 (Appendix II). In addition to the genetic plasticity of the GRNs, we found that SKN-1 requirement can be further tuned by a heritable epigenetic effect triggered by nutritional stress. This transgenerational epigenetic inheritance involves Piwi Argonaute, the nuclear RNAi pathway, and H3K9 methyltransferases (Chapter 3). Our results therefore demonstrate that the substantial plasticity in C. elegans endoderm GRN is driven by both genetic and epigenetic mechanisms.

Cover page of Novel disinfection system with recyclable magnetic nanoparticles and metal ions: evaluation with bacteria, algae and virus

Novel disinfection system with recyclable magnetic nanoparticles and metal ions: evaluation with bacteria, algae and virus


Water pollution with pathogenic microorganisms is a serious threat to human health, particularly in developing countries. Although traditional disinfection technologies, such as the use of chlorine-containing substances, ozone and UV radiation, are effective to control microorganism contamination in water sources, they present some disadvantages such as the generation of disinfection byproducts or high energy consumption, which are major concerns when considering their sustainable use. Thus, this dissertation proposes a novel disinfection system employing metal ions as disinfectants, which can be recovered using magnetic nanoparticles to reuse the disinfecting agents. Various microorganisms, including bacteria, toxic cyanobacteria and waterborne virus are used for case studies to evaluate the efficacy of this novel method, and the reusability of disinfectants and magnetic nanoparticles are explored for long term application.Chapter II presents a sustainable disinfection method with recyclable metal ions and magnetic nanoparticles applied to E. coli K12. The disinfection ability of Ag+, Cu2+ and Zn2+ were evaluated. Ag+ performed best to inactivate E. coli K12, compared to Cu2+ and Zn2+, with minimal effect of the general water characteristics except Cl-. The concentration of residual metal ions was maintained under a safe level, according to EPA guidelines, via sorption by magnetic nanoparticles. Both the magnetic nanoparticles and metal ions can be regenerated and reused with simple operating conditions and high recovery efficiency after 5 continuous cycles, indicating that this method is very promising for practical application. Chapter III optimizes the disinfection method to address toxic cyanobacteria contamination. A combination of metal ions, namely Ag+ and Cu2+, was applied to the disinfection of cyanobacteria. The disinfection effectiveness of the combination was more effective compared to individual Ag+ or Cu2+, especially at low concentration of disinfectants and short contact time. In addition, the results in Chapter III demonstrate that the cyanotoxins produced during disinfection and the residual metal ions can be removed effectively via simultaneous sorption by recyclable magnetic nanoparticles. Chapter IV explores the disinfection of waterborne viruses with magnetic nanoparticles coated by various metal ions, including Ag+, Cu2+ and Fe3+. All three magnetic nanoparticles with metal ions can inactivate above 99% of the target viruses within 0.5 hours, and the magnetic nanoparticles with Cu2+ and Fe3+ are more suitable for large-scale application than with Ag+, considering the price of metal ions. The recovery of the nanoparticles can be easily achieved with external magnetic field, for their regeneration and recycling. The disinfection efficiency remains above 99% after 5 continuous disinfection cycles. This dissertation contributes to overcome some of the disadvantages of traditional disinfection methods in a drinking water treatment plant, providing a novel approach that recovers a large fraction of the disinfectants for reuse. It serves as a scientific reference for environmental engineers in drinking water treatment, by providing an innovative approach for disinfection of water sources contaminated with a range of pathogens.

Ultra-low-loss silicon nitride photonic integrated circuits for highly coherent lasers


The development of integrated photonics has played an important role in the proliferation of high-speed telecommunications, and has the potential to impact numerous applications including precision metrology, sensing, navigation, imaging, and computation. For virtually all such optical systems, optical loss represents a key performance metric. Achieving extremely low optical loss often requires a corresponding increase in the device footprint. This work explores a regime in integrated photonics in which optical loss at parity with the world-record-lowest loss in any integrated platform is achieved, but in a form factor that is planar, fabricated using conventional CMOS processes, and an order of magnitude smaller in footprint. While prior works with comparable loss were been limited by device size and integration limitations to single-device demonstrations, the improved silicon-nitride waveguide platform presented herein enables higher photonic integrated circuit (PIC) complexity than previously explored. These properties are used to create several novel integrated devices, including ultra-high Q and record-high finesse integrated optical resonators, a hybrid-integrated laser with fiber-laser coherence properties, a low-noise microcomb source, a single-mode Raman laser, and a twenty-three meter integrated optical delay line.

While the first part of this thesis thus explores capabilities intrinsic to integrated silicon nitride waveguides, the latter part of the thesis develops novel processing techniques to enable these high performance PICs to interface with other electrical and optical components. A deuterated silicon dioxide thin-film deposition is developed to enable integration of such silicon nitride PICs with active optical materials, enabling the demonstration of a heterogeneous laser. Piezoelectric tuning of silicon nitride resonators is also explored, which could allow silicon nitride PICs to be reconfigured with dramatically lower power consumption. Finally, a study is presented on extending the benefits and designs of ultra-low loss silicon nitride waveguides to silicon-on-insulator waveguides, which are an attractive platform for many ultra-low loss PICs due to preexisting silicon photonics infrastructure.

Three Essays on Climate Risk


Climate change is forcing a shift in the characteristics of many natural hazards. Alongside other trends–such as increasing global interdependence, the rate of population and economic growth, and widening social inequalities–the risk from climate-sensitive natural hazards presents an expanding source of danger across the world. Research and practice on climate risk began by establishing standards for assessing hazards and implementing structural solutions to mitigate consequences. The field has evolved since then to include behavioral decision making and the multidimensionality in differences among people and places as determinants of exposure and vulnerability, respectively. These paradigm shifts in selecting factors for climate risk assessment happened alongside developments in modeling divergences between statistical and perceived risk as well as policy and scientific attention towards the distribution of hazards along socioeconomic and demographic lines. This dissertation, Three Essays on Climate Risk, contributes to answering pressing questions in climate risk research. The first essay, ‘Validating Social Vulnerability in Disaster Loss Models’ suggests that climate risk assessments should account for social vulnerability but practice caution since the relative contribution of social indicators varies across climate hazards. In the second essay, ‘Social and Spatial Inequalities in Climate Hazard Distributions,’ we compared multiple inequality metrics to find that exposure heterogeneously varies across metrics by choice of demographic​ and​ geographic partitioning. Researchers should therefore carefully design studies based upon theories of inequality formation and policy relevance. Preliminary results from the third essay, ‘Measuring Climate Risk Perception with Twitter Data,’ indicate that user-generated big data may soon serve as an appropriate supplement to survey data for measuring complex socio-cognitive phenomena. These essays advance climate risk measurement & modeling, unpack geographies of climate risk, and illustrate implications of improving climate risk information.

Data-driven Methods for Evaluating the Perceptual Quality and Authenticity of Visual Media


Data-driven approaches, especially those that leverage deep learning (DL), have led to significant progress for many important problems in computer vision and image/video processing over the last decade -- fueled by the availability of large-scale training datasets. Typically, for supervised DL tasks that assess the unambiguous aspects of visual media – such as classifying an object in an image, recognizing an activity in a video – large-scale datasets can be reliably captured with human-provided labels specifying the expected right answer. In contrast, an important class of perceptual tasks deserves special attention: assessing the different aspects of the quality and authenticity of visual media. DL for these tasks can enable widespread downstream applications. However, the subjective nature of these tasks makes it difficult to capture unambiguous and consistent large-scale human-annotated training data. This poses an interesting challenge in terms of designing DL-based methods for such perceptual tasks with noisy/limited training data – which is the focus of this dissertation. We first explore DL for perceptually-consistent image error assessment, where we want to predict the perceived error between a reference and a distorted image. We begin by addressing the limitations of existing training datasets: we deploy a novel, noise-robust scheme to label our proposed large-scale dataset which is based on pairwise visual preference to reliably capture the human perception of visual error. We then design a learning framework to leverage this dataset and obtain state-of-the-art results in perceptual image-error prediction. Perceptual metrics have been vital to the advancement of deep generative models for images and videos -- which, although promising, also poses a looming societal threat (e.g., in the form of malicious deepfakes). In a separate chapter, we therefore explore a complementary question: given a high-quality video without any human-perceivable artifacts, can we predict whether it is authentic? Within this context, we specifically focus on robust deepfake detection using domain-invariant, generalizable, input features. Lastly, we find that for certain perceptual tasks, such as modeling the visual saliency of a stimulus, the only way to overcome the ambiguity/noise in the training data is to query more humans, e.g., using a gaze tracker. This tends to be onerous - especially for video-based stimuli. Hence, most existing datasets are limited in their accuracy. Considering that noise-robust dataset capture in this case can often be impossible, we design a noise-aware training paradigm for video and image saliency prediction that prevents overfitting to the noise in the training data and shows consistent improvement compared to traditional training schemes. Further, since the existing video-saliency datasets do not capture video-specific aspects such as temporally evolving content, we design a novel videogame-based saliency dataset with temporally-evolving semantics and multiple attractors of human attention. Overall, through this dissertation, we make critical strides towards robust DL for visual perceptual tasks related to visual quality and authenticity assessment.

Cover page of Dislocation dynamics in chemically and microstructurally complex metallic materials

Dislocation dynamics in chemically and microstructurally complex metallic materials


Dislocations are the main carriers of the plasticity and are the dominate deformation mechanism in metallic materials. With recent innovations in manufacturing, a number of novel metallic materials with high strength have been produced. Compared to the conventional metals, these metallic materials are more chemically and microstructurally complex. To best tailor these features for optimal performnace, it is necessary to understand how these complex factors affect dislocation dynamics, such as nucleation and propagation. Using atomistic simulations, this thesis research aims to reveal the dislocation dynamics responsible for their superior strength in three types of metallic materials, i.e., multi-principal element alloys (MPEAs) with local chemical fluctuations, metallic nanolaminates with interfaces, and irradiated metals with helium (He) nanobubbles.

First, in CoCrNi MPEA, local chemical fluctuations, i.e., lattice distortion (LD) and chemical short-range order (CSRO), play an important role in the nucleation and evolution of dislocations. Under uniaxial tensile loading, LD not only lowers the Young’s modulus and strain for nucleation of Shockley partial (SP) dislocations, but also promotes the nucleation of SP dislocations and reduces their mobility, providing enough space and time for the nucleation of nanotwinning. By contrast, CSRO enhances the Young’s modulus and critical strain to nucleate SP dislocations. Similarly, dislocations are also resisted by CSRO clusters, resulting in CSRO strengthening.

Second, in metallic nanolaminates consisting of alternating metallic layers, confined layer slip (CLS) has been proposed as the main dislocation mode. CLS involves a moving dislocation confined between the parallel interfaces. It has also been postulated that this dislocation dynamics process is affected by interface structure and layer thickness. Via atomistic simulation, it is shown that compared to coherent interfaces, the CLS of dislocations between incoherent interfaces is much more difficult. Notably the key obstruction originates from the misfit dislocations within the incoherent interfaces and it is shown that the dislocation may invoke climb to continue the CLS process. It is also found that in Nb/Nb nanolaminates with coherent interfaces, the CLS stress scales inversely with the layer thickness, as proposed by analytical models. A modified CLS model is proposed that agrees with simulation and treats the influence of the interface as an additional, layer-size-independent resistance.

Last but not least, in irradiated Cu with He nanobubbles, it is well known that these nanobubbles significantly influence both material strength and ductility. In studying the interaction between the gliding dislocation and nanobubble, it is found that instead of the conventional dislocation bypass over a He bubble, i.e., bubble cutting or dislocation climb, a new multi-step-bypass (MSB) maneuver occurs. It is demonstrated that this MSB mechanism operates even at room temperature when the He atom density in the bubble is sufficiently large and the ratio of the bubble spacing to its diameter is sufficiently low. For MSB, the entire dislocation changes its glide plane to overcome the bubble, which is promoted by higher temperatures and larger He atom density in the bubble. Compared to the conventional bypass modes, MSB is more energetically favorable.

The dislocation dynamics mechanisms discovered in this research can help to deepen understanding of microstructure/performance relationships and guide the microstructure design of these high performance structural materials.

Studying chemical and biological systems using high-throughput sequencing: analytical challenges and solutions


High-throughput sequencing (HTS) can identify unique DNA sequences and quantify their abundances from mixed DNA pools. HTS-based assays can profile complex biological or chemical systems with entities that can convert to unique DNA sequences. Computational models are also developed to analyze these HTS data at a larger scale. However, such data contain unique analytical challenges, including discrete counts, relative measurement, and small sample size. Careful assessments of these computational tools are required for robust interpretations of results.

In this dissertation, we investigated the computational challenges, proposed and assess the solutions for two applications of HTS-based assays. In the first work, we proposed k-Seq, a kinetic assay to measure the activity of self-aminoacylation ribozymes (catalytic RNA). Characterizing the kinetics for different molecules in a heterogeneous pool is challenging as their abundance and activities can vary in several orders of magnitude. We explored different designs of experiments and identified critical factors affecting the estimation of kinetic coefficients in the pseudo-first-order kinetic model for these ribozymes. Using bootstrapping, we robustly quantified the uncertainty of estimation for individual sequences and determined the minimum sequencing counts required for reliable estimations. Combining the improved experimental design and new analytical tools, we robustly quantified the kinetics for 10^5 different ribozymes.

In the second work, we constructed the correlation networks between microorganisms from metagenomic data and studied the structure of a human skin microbiome in patients with chronic wounds. We designed a variation of Gaussian graphical models to capture the direct correlations between the abundances of bacteria and viruses while accounting for the structure and limitations in the data. To minimize the discovery of false correlations from the small noisy dataset, we applied a two-step model selection to regularize the results. Lastly, we demonstrated the utility of the constructed correlation network in recovering the strong correlations between microbes, identifying potentially important microbes, and microbial clusters.

Integrated Electronics for Energy-Efficient Direct and Coherent Detection in Data Center Optical Interconnects


Data centers are the pillars of modern internet infrastructure that have enabled the proliferation of emerging cloud applications, social media, and artificial intelligence, among others that are becoming increasingly integrated into our everyday lives. As data center traffic continues growing at a compound annual growth rate exceeding 25\% with internal server-to-server traffic accounting for well over 70\% of the total, engineering increased capacity through optical intra-data center interconnects (IDCIs) is essential. In keeping up with capacity demands, energy efficiency improvements are required conserve the data center energy footprint. The performance of IDCIs are thus primarily characterized by two metrics: capacity defined in bits-per-second and energy efficiency defined as the cost of energy-per-bit.

This dissertation will broadly cover the design and measurement results of energy-efficient front-end receiver integrated circuits (RXICs) and transmitter integrated circuits (TXICs) for high data rate IDCIs.

In Part I of this thesis, a comprehensive benchmarking approach for optical receivers with implications for link power consumption in both direct and coherent detection-based links will be introduced. For direct detection links, RXIC designs operating up to 64 Gbps at less than 2.53 pJ/bit will be presented and compared with the state of the art. For coherent detection links, an I-Q Costas-based optical RXIC operating up to 100 Gbps at less than 5.34 pJ/bit will be discussed and compared to the state-of-the-art. The RXICs are implemented in 130 nm SiGe HBT technology.

Part II of this thesis will investigate the energy efficient implementations of two-tap feedforward equalizers (FFEs) for high-speed optical TXICs in direct- and external-modulation schemes. For directly modulated, Vertical Cavity Surface Emitting Laser (VCSEL) transmitters, a TXIC implemented in 130 nm SiGe HBT technology is reported for data rates up to 60 Gbps at an energy efficiency less than 2.85 pJ/bit. For externally modulated, Mach Zehnder Modulator (MZM) transmitters, a TXIC implemented in 45 nm CMOS SOI technology is reported for data rates up to 80 Gbps at an energy efficiency less than 3.9 pJ/bit.

Cover page of Efficient In-DRAM Near-Bank Processing for Emerging Parallel Computing Workloads

Efficient In-DRAM Near-Bank Processing for Emerging Parallel Computing Workloads


Despite the success of parallel architectures and domain-specific accelerators in boosting the performance of emerging parallel workloads, contemporary computer organizations still face the bottleneck of data movement between processors and the main memory. Processing-in-memory (PIM) architectures, especially those designs integrating compute logics near DRAM memory banks, are promising to address this bottleneck. However, such an in-DRAM near-bank integration faces hardware and software design challenges in performance, area overheads, architecture complexity, and programmability.

To address these challenges, this dissertation focuses on developing efficient hardware and software solutions for in-DRAM near-bank computing. First, this dissertation investigates the memory bandwidth bottleneck of contemporary hardware platforms through in-depth workload characterization, which motivates in-DRAM near-bank processing solutions. Second, this dissertation proposes multiple full-stack in-DRAM near-bank processing solutions targeting different application scopes that vary from application-specific to general-purpose computing. These solutions reveal a wide spectrum of trade-off points among hardware efficiency, architecture flexibility, and software complexity. On top of these solutions, this dissertation introduces an open-source simulation framework that supports the architectural and software optimization studies of in-DRAM near-bank processing. Finally, this dissertation develops novel machine learning-based compiler optimizations for partitioning workloads on a chiplet hardware platform that has a distributed compute-memory abstraction similar to in-DRAM near-bank architectures.