Dissertations / Theses on the topic '090699 Electrical and Electronic Engineering not elsewhere classified'

Land use constraints have motivated investigation into the spatial coexistence of solar photovoltaic electricity production and agricultural production. Previous work suggests that agriculture-photovoltaic (agrivoltaic) systems either decrease crop yield or are limited to shade-tolerant crops. Existing experimental work has also emphasized fixed south-facing configurations with traditional commercial panel shapes, and modeling work is sparse. In this work, the effects of different photovoltaic array configurations and panel designs on field insolation spatial and temporal variation are explored in detail to determine photovoltaic design routes that may increase expected crop yield in agrivoltaic systems. It is found that photovoltaic row orientation is the most influential factor on insolation homogeneity due to shadow migration paths. Additionally, it is shown that utilization of mini-modules in patterned panel designs may create more optimal conditions for plant growth while using the same area of PV, thus improving the land efficiency ratio of the agrivoltaic system. Different solar tracking algorithms are explored to optimize the trade-off between electricity production and expected crop growth. The feasibility of select agrivoltaic systems is explored for multiple U.S. locations. This thesis concludes with recommendations for photovoltaic system designs corresponding with specific crop growth considerations.

Increasing demand for higher power density in many applications such as Hybrid Electric Vehicles (HEVs) and renewable power generation has led to great technological advances in power electronics. To meet this increasing demand, a power semiconductor device needs to have low on resistance, increased switching speeds and reduced total system cost. Silicon (Si) power devices have been used for several decades but they are fundamentally limited by material properties. Silicon carbide (SiC) as a power semiconductor material offers superior electrical and thermal properties compared to silicon, which it can replace in a large spectrum of applications. Because of a lower critical electric field, drift regions in Si power transistors need to be much thicker and more lightly doped, which in turn increases the specific onresistance Ron,sp. To combat the drift resistance component for higher blocking voltages, superjunction MOSFETs for medium voltages and Si IGBTs for high voltages are used. Since IGBTs are bipolar transistors, they exhibit much higher switching energy losses than MOSFETs. The SiC MOSFET is an excellent candidate in the medium to high voltage range, which mainly targets the HEV market.

Due to their low channel mobility, SiC MOSFETs have not reached the theoretical limit below 1200 V where channel resistance is dominant. Planar DMOSFETs dominate the

commercial SiC market today because of higher yield and relatively simpler fabrication process, but trench MOSFETs can be made with a smaller cell area and thus lower Ron,sp. Due to lower cell-pitch and high integration density of trench-gate devices, they offer an opportunity to reduce the size and weight of HEV power control units by replacing IGBTs with MOSFETs. The single-trench UMOSFET was first reported in 1994 by CREE and the first oxide protected trench MOSFET in 1998 by Purdue. This structure inserts a grounded p-type region below the gate trench to protect the oxide in the blocking state. In 2012, Rohm Semiconductor reported a novel double-trench UMOSFET with separate gate and

field-protection trenches. In 2017, Infineon published their new trench UMOSFET, known as Cool-SiC, with high gate oxide reliability. In this work a deeply-scaled, fully-self-aligned trench MOSFET is fabricated and characterized. The innovative process described enables a record cell-pitch of 0.5 μm per channel, equivalent to a channel density 6Å~ higher than currently available commercial UMOSFETs.

Devices that can process more information in reduced dimensions are essential for an increasingly information- and efficiency-driven future. To this end, nanocomposites are promising due to their inherent multifunctional properties and special behavior at the nanoscale. Vertically aligned nanocomposites (VANs) are particularly interesting because of their ability to self-assemble into anisotropic nanostructures and high density of heterointerfaces – characteristics which introduce unique functionalities and offer exciting new avenues for device applications. However, a vast majority of VAN systems are currently fabricated on single-crystal oxide substrates, which may be cost-prohibitive at large scales and are generally incompatible with the prevalent device fabrication techniques. Thus, integration of VAN thin films on silicon becomes a critical step toward implementing VANs in a well-established semiconductor manufacturing industry.

In this dissertation, the viability of oxide-metal and nitride-metal VAN thin films integrated on silicon substrates has been demonstrated through a set of unique buffer layer designs. For the first three systems presented in this dissertation, namely, LaSrFeO₄-Fe, BaTiO₃-Au, and BaTiO₃-Fe, microstructural and physical property (i.e. electrical, magnetic, and optical) analyses confirm their successful epitaxial growth on silicon, with only minor differences compared to their counterparts grown on single-crystal oxide substrates. For the fourth system, a new and robust TiN-Fe VAN has been proposed and demonstrated. The new TiN-Fe VAN system on Si exhibits superior magnetic properties and unusual optical properties. With further growth optimization and/or patterning techniques, VAN thin film integration on silicon presents itself as a feasible and cost-effective approach to designing electronic, spintronic, photonic, and sensing devices.

The miniaturization of a MOSFET is the constant driving force in semiconductor technology over the decades. This scaling enables the realization of the ever complex and functional integration on a single chip where over tens of billions of transistors densely packed. Silicon (Si) is always the golden performer until recent years when the shrinking of a transistor becomes more and more difficult, due to phenomena such as short channel effect and mobility degradation, which is a challenge especially for atomic level scaling. The dawning of low dimensional materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus (BP), with their natural atomically thin two-dimension (2D) layered structure and other novel properties, might serve as an alternative solution for ultimate scaling. However, the understanding of the electronic transport in these Van der Waals materials is still lacking.

In this research, the exploration of this material was first initiated on the vertical heterojunctions where two materials’ interfaces meet. Many previous literatures claimed this hetero-interface creates a P/N junction that results in a diode-like rectification. Yet, by careful analysis and comparing with our “real” vertical structures where the lateral components were eliminated, it is proved this rectification is a direct result from the contact region. The Schottky barrier on the drain side together with the gate effect is the true culprit.

Realizing how the Schottky barrier could be dominating in these 2D FETs, the second study is the Schottky barrier effect on the contact resistances and furthermore the mobility of the device. Because of the existence of the Schottky barrier between the channel and contact, the contact resistance is not negligible, unlike the ohmic contact for conventional Si MOSFETs. By comparing the intrinsic and extrinsic mobilities of TMD materials, It is found that the contact resistance’s response to the back gate, namely, the rate of how it changes with the back gate has a huge factor in determining whether the extrinsic field-effect mobility underestimates or overestimates its intrinsic mobility. This opens a new insight on the understanding of the transport mechanism under contacts for different TMDs.

In this study, a novel and systematic methodology for the design and optimization of conductor-backed coplanar waveguide (CB-CPW) based low pass filter (LPF) is proposed. The width of the signal trace is continuously varied using a truncated Fourier series, and the adjacent gaps are designed in several types established on a specific optimization setup to obtain predefined electrical characteristics with maximum compactness taking into account physical constraints. Trust-region-reflective algorithm (TRRA), genetic algorithm (GA), and particle swarm optimization algorithm (PSO) are taken into account to minimize the developed bound-constrained non-linear objective function respectively.

All types are programmed and analytically verified in MATLAB. Solutions include design parameters such as the physical length and width of the structure, which will be drawn in AutoCAD later on. Also, the optimized layouts are exported to Ansys High Frequency Structure Simulation (HFSS) software for simulation and validation. Non-uniform CB-CPW LPFs are optimized and simulated over a frequency range of 0-6 GHz with a cutoff frequency of 2 GHz. Simulation results show a good agreement with the analytical ones.

Measurement of physiological deformations in specific tissues can provide significant information for the diagnosis, monitoring, and treatment of medical conditions. Yet these deformation measurements can be hard to obtain, especially when the targeted tissue is inside the body where optical access is denied. Current medical imaging technologies, including ultrasound, magnetic resonance imaging (MRI) and X-ray, can image soft tissues and bones with decent spatial resolution. However, they are not feasible for chronic tissue monitoring or cases in which rapid tissue deformation/vibration measurements are required. Wireless magnetic sensing is a favorable option for implantable pressure, strain, or deformation sensing systems due to its compact size, passiveness, high sampling rate and minimal interference from biological materials. Polymeric magnets, made from polymer carrier and embedded magnetic micro/nano-particles, possess the traits of flexibility, stretchability and biocompatibility that are preferred for biomedical applications. Nonetheless, their magnetic field is much weaker comparing to that of traditional ferrous/rare earth magnets. Emergence of highly sensitive magnetic sensors based on various principles (Hall effect, anisotropic magneto-resistance (AMR), giant magneto-resistance (GMR), giant magneto-impedance (GMI), tunneling magneto-resistance (TMR)) has enabled precise magnetic sensing of such polymeric magnets. To this end, we developed wireless magnetic sensing systems capable of measuring tissue deformations through implantable polymeric magnets for biomedical applications. This thesis work details the end-to-end development (magnetic sensor selection, magnetic transducer design & fabrication, measurement algorithm development) and the collaborative, interdisciplinary experiment result of a wireless brain deformation sensing system for blast induced traumatic brain injury (bTBI) featuring a polymeric magnetic disk, and a wireless strain sensing system for bladder dysfunction or heart failure (HF) featuring a stretchable polymeric magnetic band. Both systems comprise of one or more polymeric magnetic transducers, an external magnetic sensor / sensor array, and a signal processing unit. Upon tissue deformation, the magnetic transducers attached to the tissue deform jointly, inducing a change in the magnetic field that can be measured wirelessly by the external magnetic sensor / sensor array. Tissue deformation is then recovered from the measured magnetic field signal via the signal processing unit.

Digital image processing techniques have many significant applications in industry. In this thesis, we focus on three problems in digital image processing. These three problems involve halftone images, information encoding and decoding, image alignment, and deep learning.

Specifically, the first problem is based on data-bearing halftone images, which are an aesthetically pleasing alternative to barcodes. We address the issues generated in the camera captured image alignment process. We perform some theoretical analysis and validate it by simulation. We also provide an optimal solution to the problem.

The second problem is about the alignment technique on a 3D surface. We develop a pipeline of surfaces coding to solve the alignment issues on 3D surfaces, which includes oblique surfaces and cylindrical surfaces.

The third problem is related to image retrieval. We propose a deep learning based solution to the fashion image retrieval task. Fashion image retrieval is significant to improve the customers’ experience in online shopping. A fast, accurate shopping item information retrieval system based on the customers’ uploaded image has been built by us. A novel solution is provided, and it achieves state-of-art accuracy in shopping items’ information retrieval.

Beyond graphene, two-dimensional (2D) atomic layered materials have drawn considerable attention as promising semiconductors for future ultrathin layered nano-electronic device applications, transparent/flexible devices and chemical sensors. But, they exhibit high levels of low-frequency due to interfacial scattering (small thickness) and interlayer coupling (large thickness). The sources and mechanisms of low frequency noise should be comprehensive and controlled to fulfill practical applications of two-dimensional transistors. This work seeks to understand the fundamental noise mechanisms of 2D transistors to find ways to reduce the noise level. It also verifies how noise can provide a spectroscopy for analysis of device quality.

Most noise analysis tend to apply classical MOSFET models to the noise and electrical transport of 2D transistors, which put together all possible independent noise sources in 2D transistors, ignoring the contact effects. So this could lead to wrong estimation of the noise analysis in 2D transistors. This work demonstrates how the noise components can come from the channel and contact/access regions, all independently adding to the total noise. Each noise source can contribute and may dominate the total noise behavior under the specific gate voltage bias. Herein, the measured noise amplitude in our MoS₂ and MoSe₂ FETs shows a direct crossover from channel- to contact-dominated noise as the gate voltage is increased. The results can be interpreted in terms of a Hooge relationship associated with the channel noise, a transition region, and a saturated high-gate voltage regime whose characteristics are determined by a voltage-independent conductance and noise source associated with the metallurgical contact and the interlayer resistance. The approach for separating channel contributions from those contact/access region allows clear evaluation of the channel noise mechanism and also can be used to explain the qualitative differences in the transition regions between contact- and channel-dominated regimes for various devices.

Alzheimer’s disease (AD), is a devastating neurodegenerative disorder that destroys the patient’s ability to perform daily living task and eventually, takes their lives. Currently, there are 5.8 million people in North America that suffer from AD. This number is projected to by 13.8 million by the year of 2050. For many years, researchers have been dedicated on performing automated diagnosis based on neuroimaging. There are critical needs in two aspects of AD: 1) computer-based AD classification with MRI images; 2) computer-based tools/system to enhance the AD patient’s quality of life. We are addressing these two gaps via two specific objectives in this study.

For objective 1, the task is to develop a machine-learning based intelligent model for classification of AD conditions (Normal Control [NC], Mild Cognitive Impairment [MCI], Alzheimer’s disease [AD]) based on MRI images. Specifically, four different deep learning models were developed and assessed. The overall average accuracy for AD classification is 81.5%, provided by Multi-Layer-Output model.

For objective 2, a deep learning model was developed and evaluated to recognitze three specific type of indoor scenes (bedroom, living room and dining room). An accuracy of 97% was obtained.

This study showed the potential of application in deep learning models for two different aspects of AD - disease classification and intelligent model-based assistive device for AD patients. Further research and development activities are recommended to further validate these findings on larger and different datasets.

Most foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In food handling, there exist steps to achieve good manufacturing practices (GMPs). Traditionally, the assessment of food handling quality would require hiring a food expert for audit, which is expensive in cost. Recently, recognizing activities in videos becomes a rapidly growing field with wide-ranging applications. In this presentation, we propose to approach the assessment of hand-hygiene quality, which is a crucial step in food handling, with video analytic methods: action recognition and action detection algorithms. Our approaches focus on hand-hygiene activities with different requirements include camera views and scenario variations.

Emotional regulation is how people manage their emotions especially anxiety, anger, and frustration, which are all negative emotions. It is critical to health, academic achievement, and work performance to have proper emotion regulation skills. In order to facilitate participants to manage emotions, we developed a series of training programs by using HTC^© Vive^TM headset and Neuracle. The HTC Vive is to improve immersion in presence to lead to more effective training, and the Neuracle is using Electroencephalography (EEG) techniques for reading user’s brainwave signals which provide real time input for the training programs. We focused on analyzing if emotion, which was reflected in brainwave signals, had changes when participants were exposed to positive/negative stimuli. The testing results indicated that there were noticeable changes in brainwave signals to stimuli. The findings from the testing provide a solid foundation to use brainwave signals as real-time input in our game development for improving emotion regulation skills in the future.

Metasurfaces are broadly defined as artificially engineered material interfaces that have the ability to determinately control the amplitude and phase signatures of an incident electromagnetic wave. Subwavelength sized optical scatterers employed at the planar interface of two media, introduce abrupt modifications to impinged light characteristics. Arbitrary engineering of the optical interactions and the arrangement of the scatterers on plane, enable ultra-compact, miniaturized optical systems with a wide array of applications (e.g. nanoscale and nonlinear optics, sensing, detection, energy harvesting, information processing and so on) realizable by the metasurfaces. However, maturation from the laboratory to industry scale realistic systems remain largely elusive despite the expanding reach and vast domains of functionalities demonstrated by researchers. A large part of this multi-faceted problem stems from the practical constraints posed by the commonly used plasmonic materials that limit their applicability in devices requiring high temperature stability, robustness in varying ambient, mechanical durability, stable growth into nanoscale films, CMOS process compatibility, stable bio-compatibility, and so on.

Aiming to create a whole-some solution, my research has focused on developing novel, high-performance, functional plasmonic metasurface devices that utilize the inherent benefits of various emerging and alternative material platforms. Among these, the two-dimensional MXenes and the refractory transition metal nitrides are of particular importance. By exploiting the plasmonic response of thin films of the titanium carbide MXene (Ti₃C₂T_x) in the near infrared spectral window, a highly broadband metamaterial absorber has been designed, fabricated and experimentally demonstrated. In another work, high efficiency photonic spin Hall Effect has been experimentally realized in robust phase gradient metasurface devices based on two different refractory transition metal nitrides –titanium nitride (TiN) and zirconium nitride (ZrN). Further, taking advantage of the refractory nature of these plasmonic nitrides, a metasurface based temperature sensor has been developed that is capable of remote, optical sensing of very high temperatures ranging up to 1200^oC.

Undergraduate engineering students usually face difficulties understanding electric circuit concepts. Some of those difficulties regard with misconceptions students bring into the classroom and develop during the learning process. Additionally, the increasing complexity of the topics along the fundamental electric circuits course constitutes another factor to those difficulties students experience. Another component we can add to this equation consists of the need of modernize and actualize the curriculum to meet the society’s demands of the next taskforce. Therefore, it is important to investigate the conceptual difficulties students experience when they analyze complex electric circuits. In this dissertation, I identify what those conceptual difficulties are when undergraduate sophomore engineering students attempt to analyze solid-state device circuits. The context of this research comprises a modernized version of the traditional fundamental electric circuits course. This modernized version includes DC analysis, 1^st order transient analysis, AC, and solid-state device analysis.

This dissertation took the form of three individual but complementary studies. Each study contributes to partially answer the overall research question. However, each study answered its own research problem. The first study attempted for identifying what concepts beginning students find challenging regarding semiconductors physics, diodes, and transistors. The second study identified student’s misconceptions when they analyze two solid-state device circuits, one with a diode, and the other with a transistor. The final study looked for determining what misconceptions students use at both earlier and more advances stages along the course. This study also searched for understanding how students move through conceptual changes along the semester.

The general findings comprise three main points. First, students bring misconceptions into the classroom probably built from their previous experiences. Second, they also can develop those misconceptions through the learning process. This is particularly key regarding the relatively new and complex topics from student’s perspectives. Finally, language plays an important role on the kind of misconceptions students develop. How students perceive the professional community use language contributes to either consolidate or modify old misconceptions or develop new ones.