As CMOS technology generations advance, the well structure has greater influence on single-event (SE) charge collection processes. Through the use of full 3D TCAD simulations, the effects of well structure are investigated by studying the temporal and spatial characteristics of well potential modulation (WPM). It is shown that the conductivity of the well and the amount of well collected charge are major factors in determining the SE WPM response. WPM is characterized across many layout and technology process parameters to help explain this phenomenon. A set of measurement circuits are proposed that could potentially be used to characterize WPM in any bulk CMOS technology generation. Lastly, the effect on SE mechanisms of anti-puncthrough implants is explored in TCAD simulations. It is shown that some anti-punchthrough implants can affect SE charge collection in pFETs.

Depression is a potentially life threatening mood disorder which affects many people. Two thirds of the people with depression don‟t realize that depression is a treatable illness, only 50% of people diagnosed with major depression receive any kind of treatment, and only 20% of them get treatment. Depression can lead to suicidal behavior. It is very important to realize that depression is a treatable disorder and suicide is a preventable act. A recent research study reported a frightening result which was that 48% of patients who have suicidal ideations and 24% of those who have committed suicide did not receive any care or even perceive the need for care. Therefore, it is very important to evaluate a patient‟s psychological state and to evaluate a depressed patient‟s risk of committing suicide, since suicide may be prevented by the psychiatric help. A unique challenge is discriminating the high risk suicidal (HR) patients from the depressed (DP) patients and this dissertation is focused on tackling this challenge. In this dissertation, two different types of audio recordings from the depressed patients (diagnosed with depression), the high risk suicidal patients (diagnosed with high risk suicide), and the remitted patients (diagnosed with remission from the depression) were gathered and analyzed. One type is audio recordings that were gathered from the clinical interviews (interview session); the other one is gathered while the patients were reading a predetermined passage (reading session). This dissertation presents three different studies. In the first study, mel-frequency cepstral coefficients (MFCCs) are used to estimate suicidal risk using different numbers of MFCCs with and without environmental compensation. A different approach is proposed to maximize the classification rates of discriminating the high risk suicidal patients from the depressed patients using fewer coefficients. The aim of this research is estimating the suicidal risk using MFCCs with high classification rates and the results show that the MFCCs are useful indicators for DP-HR discrimination. In the second study, we propose various approaches to maximize the classification rates of discriminating the high risk suicidal patients from the depressed patients using power spectral density features. In earlier studies, 4 fixed energy bands which are uniformly placed band edges in the 0-2000 Hz frequency range (0-500 Hz, 500- 1000 Hz, 1000-1500 Hz, 1500-2000 Hz) were analyzed. In this study, various optimization techniques are used which are increasing the number of energy bands, increasing the energy band range, increasing the energy band number & range, exponential band edges, exponential band edges & increasing the energy band range, non-uniform band edges, and finally non-uniform band edges & increasing the energy band range. It is found that these approaches provide better classification rates for discriminating the high risk suicidal patients from the depressed patients. In the last study, gender specific differences on optimized energy bands are investigated. There exist statistically significant gender differences in the Depressed (DP) and the High Risk Suicidal (HR) pairwise group during the interview and reading sessions. 14 statistically significant features are found during the interview session, and 4 statistically significant features are found during the reading session. There are no statistically significant gender differences in the High Risk Suicidal-Remitted (HR-RM) pairwise group during the interview session and during the reading session. There exist statistically significant gender differences in the Depressed (DP) and the Remitted (RM) pairwise group during the interview and reading sessions. 26 statistically significant features are found during the interview session, and 2 statistically significant features are found during the reading session. Spontaneous speech (interview session) is more effective for revealing gender differences than the controlled reading speech (reading session).

The reliability of GaN/AlGaN HEMTs, fabricated using MOCVD, and MBE under Ga-rich, N-rich and ammonia-rich conditions, is studied using high field stress experiments and low frequency 1/f noise measurements. Hot electron stress results in positive shifts in pinch-off voltage for Ga-rich and N-rich devices, and negative shifts for devices grown using MOCVD and ammonia-rich MBE. Density functional theory (DFT) calculations suggest that dehydrogenation of Ga-vacancies is responsible for the positive shift in pinch-off, while the dehydrogenation of N-antisites causes the negative shift. Low frequency 1/f noise measurements show that dehydrogenation of N-antisites causes noise to increase in devices after stress, while dehydrogenation of carbon impurities causes noise to decrease after stress. Noise in GaN devices, when measured at high current conditions, obeys the Dutta-Horn model. Temperature-dependent noise measurements show a peak at 0.2 eV in all devices. The peak corresponds to the reconfiguration of an oxygen DX center in AlGaN. A second peak at high temperatures is seen in N-rich devices, which corresponds to the reconfiguration of N-antisites in AlGaN. Irradiation using 1.8 MeV protons causes positive shift in pinch-off voltage and increase in noise in all devices; these shifts are caused by the creation of Ga-N divacancies during proton irradiation.

Two-photon absorption (TPA) has become an important tool for understanding the mechanisms of single-event effects (SEE) in microelectronics. Very little experimental work has been reported that describes the physical structure of the charge cloud centered on the focal plane where TPA carrier generation is likely to occur (the "TPA region"). A detailed study of the TPA region would provide valuable insight into the use of TPA as an SEE testing method in general. This work reports measurements of the spatial extent of the TPA region in the direction of beam propagation in silicon diodes. Experimental results indicate that the waist diameter of the beam can have a significant impact on the spatial qualities of the TPA region and that in some circumstances, peak charge collection occurs when the focal plane of the laser is several micrometers below the junction. The implications that these results could have for TPA SEE laser testing will also be discussed. A simple analytical model for TPA in a semiconductor is implemented using device level simulations. Results indicate that while the simple model is adequate for describing qualitative trends, it is not suitable for quantitative studies.

Orbiting spacecraft experience harsh radiation environments that may affect microelectronics in undesirable ways. Ionizing radiation interacts with microelectronics in a variety of ways. One such effect is known as single event effects (SEEs), which is the response of semiconductor device to a single ionizing particle event. Ensuring the integrity of scientific data in harsh radiation environments is critical for the success of missions that require recording and storing large volumes of data. A single event upset (SEU) is an erroneous change in the state of the memory cell. The radiation effects community has developed many experimental and simulation based techniques to determine the sensitivity of memory cells to SEUs. The role of ion track structure has been a concern in the SEEs community for more than twenty years. Linear energy transfer (LET), the rate of energy lost by the incident ion per unit path length within a material, has been used to relate the space environment to the ground test environment and has been the traditional metric for much of SEE analysis. In more recent years, additional physical mechanisms, for example SEUs resulting from proton direct ionization and nuclear reactions, have been required to describe the conventional cross section versus LET curves obtained by ground based experiments. These trends suggest that technology nodes are becoming sensitive to direct ionization and effects from secondary particles produced by the incident particles track structure. Silicon-on-Insulator (SOI) technology has long been advantageous in SEE mitigation due to its small active device area and isolating buried oxide layer (BOX). However, these benefits are not without cost, as the threshold LET, the LET at which saturation occurs in the upset cross section, for SOI technologies is typically lower than equivalent bulk technologies. With memory cells becoming increasingly sensitive to effects from ionizing particle events, there is concern about the contribution from secondary particles related to track structure of the incident heavy ion. Energetic secondary electrons, δ-rays, are frequently generated in ionizing radiation events. These δ-rays undergo scattering events resulting in localized charge generation comparable to the critical charge of modern SOI technology nodes. In this work, we use Monte-Carlo radiation transport simulations to evaluate the impact of δ-rays on highly scaled silicon-on-insulator (SOI) technologies. A 22 nm SOI SRAM is used to estimate the geometry and critical charge. Results suggest that long-range δ-rays can deposit sufficient energy to cause single event upsets (SEUs) in SRAM cells separated by many micrometers. We discuss the implications of δ-ray induced SEU on hardening techniques and technology computer aided design device simulations.

Carbon nanotubes (CNTs) have many potential applications due to their electrical conductivity, excellent chemical properties, mechanical strength, and high surface area. Because of their unique material properties, CNTs are promising for next generation supercapacitor applications. CNTs based electrode offers exceptional energy and power performance due to the high surface area and the ability to functionalize the CNTs to optimize electrochemical supercapacitor properties. This research is focused on the synthesis of CNTs and electrochemical characterization of CNTs based electrode to improve the performance of ultracapacitors. In brief, the H2/NH3/CH4 (75:75:25 sccm) and H2/NH3/CH4 gas mixture (75:100:25 sccm) for different growth time (2, 4, 6, 8, and 10 min) were applied to CNT synthesis using HFCVD. Growth characteristics of carbon nanotubes changed from spaghetti-like to vertical aligned when the growth condition was switched from low to high ammonia ratio. The length of carbon nanotubes changed from ~2 ìm to ~50 ìm when the growth time lengthened. Post synthesis treatment of CNTs including polyvinyl alcohol (PVA) treatment, H2O2 (hydrogen peroxide) treatment, H2 (hydrogen) plasma treatment, were performed and used to improve the CNTs electrodes capacitance. The encouraging results demonstrated that polyvinyl alcohol (PVA) could be used to functionalize carbon nanotubes and transform the as grown hydrophobic nanotube surfaces into desirable hydrophilic surface to enhance the capacitive performance. On the other hand, both hydrogen peroxide treatment and hydrogen plasma treatment were found not to be useful for modifying the carbon nanotubes for ultracapacitor application.

The superior material properties of carbon nanotubes (CNTs) such as high aspect ratio, high surface area, superb mechanical properties, thermal and chemical stability have generated enormous research activities to push for their potential applications. This study focuses on the fabrication and characterization of CNTs synthesized by hot filament chemical vapor deposition and evaluates their use as electrochemical biosensor. The as grown CNTs were first characterized for their electrochemical behavior in ferrocyanide redox reaction using cyclic voltammetric analysis. Then, their use for dopamine detection was investigated. Well-established redox reaction current peaks for dopamine/o-quinone and the secondary redox reaction current peaks for leucodopominechrome/dopaminechrome were reproducibility observed with distinct redox current peak increments as a function of the bioanalyte concentration. These results were also confirmed by Cottrell and Nernst equations. In general, the results have demonstrated that CNTs are better than graphite as biosensor electrode for the detection of dopamine.

This dissertation presents studies on, and from, the development of algorithms for the automatic segmentation of the structures of the ear. Many otological procedures would benefit from a system that automatically identifies anatomical structures of the ear in CT. Conventional (registration-based) segmentation techniques are suitable for identifying ear structures that have high intensity contrast in CT or those for which a high degree of accuracy is not necessary. For some ear structures, conventional techniques are inadequate, and other segmentation methods must be used or developed. In this dissertation, approaches that permit the automatic identification of ear structures will be presented. This will include the ossicles, external auditory canal, cochlea, scala tympani, scala vestibuli, facial nerve, chorda tympani, semicircular canals, and the carotid artery. Each of these sensitive structures lies within millimeters of the surgical approach for various types of procedures. Out of the above mentioned structures, six distinct tubular shaped structures are represented, each of which has unique properties that make detection difficult. This has led to the development of an algorithm for the segmentation of general tubular structures, which is also presented in this dissertation. This algorithm is validated on these ear structures as well as other tubular structures outside of the ear. The results of all methods presented in this work are analyzed, and quantitatively compared to expert drawn or expert edited segmentations, which are treated as the gold standard.

This work is focused on creating an autonomous location recognition system that is capable of determining its location based on the percepts observed in the environment. This process involves segmenting the percepts in the region, segmenting the global region into local regions, developing models of the local regions based on the percepts present in that region, and recognizing both the percepts and regions. The models are based on the dominant percepts found in the global region, and are refined in order to define each local area. The feature space used to define the percepts is based on the hue, saturation, and value (HSV) color space quantized into a very high dimensional feature space (e.g. 10,000 dimensions). The global region is segmented into local regions using a relative perceptual difference measure between the current image and prior images. Once the local regions and global percepts have been found, the local models for each region are created and used for the location recognition process. Furthermore, a comparison of the current methods and prior methods of clustering the very high dimensional feature space are provided, as well as a comparison of the classification methods used based on this feature space. Finally, while the system moves through the environment, the percept blobs segmented are tracked and, based on their movement, defined. This involves recognizing reflections created by distant light sources, defining all other percepts with definitions ranging from actual percepts to aberrations of light, determining novel objects, and determining novel regions.

The effects of low and medium energy x-rays on MOS capacitors with SiO2 or HfO2 gate dielectrics and Al and TaSi gate metallization have been studied using the Monte Carlo simulator, MRED. We find good agreement between our calculations and previous results in the literature with other simulators and with experiments on devices with SiO2 gate dielectrics. There is a significant dose reduction in thin HfO2 layers exposed to 10-keV x-rays, when the HfO2 is surrounded by lower-Z materials (e.g., Si, Al). We have extended our studies to MOS capacitors with HfO2 gate dielectrics and TiN and TaN metal gates. We quantify the effects of back-end-of-line (BEOL) layers, including copper interconnect, W vias, and BPSG and SiO2 passivation layers for low and medium energy x-rays. For thick metallization stacks irradiated by 10-keV x-rays, dose enhancement or attenuation can occur, depending on material type and overlayer thicknesses. In a medium-energy x-ray environment, the dose in a HfO2 gate dielectric can be ~ 10 times higher than the dose in a SiO2 dielectric, for the same incident x-ray fluence, with increasing doses due to BEOL layers.