Electrical & Computer Engineering Theses and Dissertations

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    (2023) Lashley, Stephanie; Chembo, Yanne K; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Brillouin microscopy is a non-invasive, label-free optical elastography method for measuring mechanical properties of cells. It provides information on the longitudinal modulus and viscosity of a medium, which can be indicators of traumatic brain injury, cancerous tumors, or fibrosis. All optical techniques face difficulties imaging turbid media, and Monte Carlo simulations are considered the gold-standard to model these scenarios. Brillouin microscopy adds a unique challenge to this problem due to the angular dependence of the scattering event. This thesis extends a traditional Monte Carlo simulation software by adding the capability to simulate Brillouin scattering in turbid media, which provides a method to test strategies to mitigate the effects of multiple elastic scattering without the time and cost associated with physical experiments. Experimental results have shown potential methods to alleviate the problems caused by multiple elastic scattering, and this thesis will verify the simulation results against the experimental findings.
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    (2023) Khan, Muhammad Raziuddin A.; Iliadis, Agis; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Betavoltaic devices are p-n/p-i-n junction diodes that use the kinetic energy of beta (electron) particles emitted by beta isotopes to create electron-hole pairs and generate electrical power in a similar way as photovoltaic devices use photons energy to generate electrical power. Unlike photovoltaic devices (solar cells), betavoltaic devices can generate electrical power day and night continuously for decades (12 years with 3H (tritium) and 100 years with Ni63 (nickel-63)), enabling new capabilities that are not possible with photovoltaic devices or current state of the art chemical batteries. New capabilities include decade-long continuous power for unattended sensor, tagging/tracking devices, and other electronics placed in remote areas (underwater, polar region, space, etc) where change/charge of batteries is highly inconvenient or impossible. It is expected that wide band gap semiconductors like GaN with an energy gap of 3.4 eV may provide a better performance in terms of output power and stability under beta radiations. However, GaN semiconductor technology is still maturing in terms of growth and fabrication techniques. InGaP has a moderately wide bandgap of 1.86 eV, but it is well advanced in terms of crystal growth and fabrication techniques. Therefore, our study and research focused on the development (design, fabrication, evaluation) and comparison of a wide band gap (GaN) and a moderately high band gap InGaP, that are considered very promising in betavoltaic applications. Betavoltaic devices were fabricated on three GaN p-i-n structures with different i-layer thicknesses (600 nm, 700 nm and 1 µm). Two GaN p-i-n structures were grown on top of a sapphire substrate and the third GaN structure was grown on top of a bulk GaN substrate. InGaP devices were fabricated on an InGaP n-i-p structure grown on top of a gallium arsenide (GaAs) substrate. Devices were characterized using current - voltage (IV) measurements in the dark, using a UV light source, and under an electron beam stimulus to mimic their performance under real beta isotopes. Dark IV measurements confirmed good quality diodes with low leakage currents, and IV curves under the UV light (365nm, 3.40 eV) source showed a clear photo-response. IV curves under the electron beam irradiation at 16 KeV (average energy emission of Ni63 beta source at 250 mCi/cm2) resulted in the output powers of 3.01 µW/cm2 with an efficiency of 12.63 % for the InGaP device, and 3.32 µW/cm2 with an efficiency of 13.2% for the GaN device. InGaP and GaN devices were also exposed under a 4.5 MeV alpha beam to determine their suitability for an alphavoltaic power source (Direct energy conversion). Both InGaP and GaN devices showed degradation in their MPPs under the direct alpha beam exposure. We also investigated an indirect alpha-photovoltaic (APV) power source by employing ZnS phosphor as an intermediate layer to limit this degradation. This ZnS layer absorbs all the alpha energy and converts it into photons to create EHPs in the semiconductor device to generate electrical output power via indirect energy conversion. We determined that even though APV approach prevented radiation damage in the semiconductor device but the degradation rate of ZnS phosphor is faster compared to the degradation rate of GaN and InGaP devices under direct alpha beam exposure.
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    (2023) Wang, Fakai; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    As the aging population and related health concerns emerge in more countries than ever, we face many challenges such as the availability, quality, and cost of medical resources. Thanks to the development of machine learning and computer vision in recent years, Deep Learning (DL) can help solve some medical problems. The diagnosis of various diseases (such as spine disorders, low bone mineral density, and liver cancer) relies on X-rays or Computed Tomography (CT). DL models could automatically analyze these radiography scans and help with the diagnosis. Different organs and diseases have distinct characteristics, requiring customized algorithms and models. In this dissertation, we investigate several Computer Aided-Diagnosis (CAD) tasks and present corresponding DL solutions. Deep Learning has multiple advantages. Firstly, DL models could uncover underlying health issues invisible to humans. One example is the opportunistic screening of Osteoporosis through chest X-ray. We develop DL models, utilizing chest film to predict bone mineral density, which helps prevent bone fractures. Humans could not tell anything about bone density in the chest film, but DL models could reliably make the prediction. The second advantage is accuracy and efficiency. Reading radiography is tedious, requiring years of expertise. This is particularly true when a radiologist needs to localize potential liver tumors by looking through tens of CT slices, spending several minutes. Deep learning models could localize and identify the tumors within seconds, greatly reducing human labor. Experiments show DL models can pick up small tumors, which are hardly seen by the naked eye. Attention should be paid to deep learning limitations. Firstly, DL models lack explainability. Deep learning models store diagnostic knowledge and statistical patterns in their parameters, which are obscure to humans. Secondly, uncertainty exists for rare diseases. If not exposed to rare cases, the models would yield uncertain outcomes. Thirdly, training AI models are subject to high-quality data but the labeling quality varies in clinical practice. Despite the challenges and issues, deep learning models are promising to promote medical diagnosis in society.
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    (2023) Buyukkaya, Mustafa A; Waks, Edo; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Recent developments in nanofabrication have opened opportunities for strong light-matter interactions that can enhance optical nonlinearities, both classical and non-classical, for applications such as optical computing, quantum communication, and quantum computing. However, the challenge lies in integrating these optical nonlinearities efficiently and practically with fiber-based and silicon-based photonic circuits on a large scale and at low power. In this thesis, we aimed to achieve this integration of classical and quantum optical nonlinearities with fiber-based and silicon-based photonic circuits.For classical optical applications, optical bistability is a well-researched nonlinear optical phenomenon that has hysteresis in the output light intensity, resulting from two stable electromagnetic states. This can be utilized in various applications such as optical switches, memories, and differential amplifiers. However, integrating these applications on a large scale requires low-power optical nonlinearity, fast modulation speeds, and photonic designs with small footprints that are compatible with fiber optics or silicon photonic circuits. Thermo-optic devices are an effective means of producing optical bistability through thermally induced refractive index changes caused by optical absorption. The materials used must have high absorption coefficients and strong thermo-optic effects to realize low-power optical bistability. For this purpose, we choose high-density semiconductor quantum dots as the material platform and engineer nanobeam photonic crystal structures that can efficiently be coupled to an optical fiber while achieving low-power thermo-optical bistability. For applications that require non-classical nonlinearities such as quantum communication and quantum computing, single photons are promising carriers of quantum information due to their ability to propagate over long distances in optical fibers with extremely low loss. However, the efficient coupling of single photons to optical fibers is crucial for the successful transmission of quantum information. Semiconductor quantum dots that emit around telecom wavelengths have emerged as a popular choice for single photon sources due to their ability to produce bright and indistinguishable single photons, and travel long distances in fiber optics. Here, we present our advances in integrating telecom wavelength single photons from semiconductor quantum dots to optical fibers to realize efficient fiber-integrated on-demand single photon sources at telecom wavelengths. Finally, using the same methodology, we demonstrate the integration of these quantum dots with CMOS foundry-made silicon photonic circuits. The foundry chip is designed to individually tune quantum dots using the quantum confined stark shift with localized electric fields at different sections of the chip. This feature could potentially enable the tuning of multiple quantum emitters for large-scale integration of single photon sources for on-chip quantum information processing.
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    (2023) Castro, Alexander Victor; Abshire, Pamela A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This work focuses on the design of a capacitance-to-digital converter (CDC) for high-speed high-resolution readout of capacitive sensors. Most previously reported CDCs show a tradeoff in resolution and conversion speed; In this work a two-step successive approximation register (SAR) CDC is proposed to improve resolution and conversion speed over state-of-the-art. First, the coarse conversion stage performs a capacitive offset compensation down to within 10fF. The fine conversion stage converts the amplified residue voltage with a resolution of 200aF. These bits are communicated off chip on an I2C bus. The effective number of bits (ENOB) is compared under different measurement conditions. The circuit achieves 9.8 ENOB with a 28 µs conversion time. When overclocked, the circuit achieves 8.2 ENOB with a 14 µs conversion time. This equates to an overall figure of merit (ENOB throughput) of 350 kbits/s and 585 kbits/s, respectively, which is among the highest values reported in the literature. The interface circuit design is described, simulated, and measured to characterize performance.