IDDQ testing is one of the most effective methods for detecting defects in integrated circuits. Higher leakage currents in more advanced semiconductor technologies have reduced the resolution of IDDQ test. One solution is to use built-in current sensors. Several sensor techniques for measuring the current based on the magnetic field or voltage drop across the supply line have been proposed. In this work, we develop a behavioral model for a built-in current sensor measuring voltage drop and use this model to better understand sensor operation, identify the effect of different parameters on sensor resolution, and suggest design modifications to improve future sensor performance.

Noise and linearity are the two key concerns in RF transceiver systems. However, the impact of circuit topology and device technology on systems noise and linearity behaviors is poorly understood because of the complexity and diversity involved. There are two general questions that are addressed by the RF device and circuit designers: for a given device technology, how best to optimize the circuit topology; and for a given circuit topology, how best to optimize the device technology to improve the noise and linearity performance. In this dissertation, a systematic noise and linearity calculation method is proposed. This approach offers simple and analytical solutions to optimize the noise and linearity characteristics of integrated circuits. Supported by this approach, the physics of state-of-the-art SiGe HBT technology devices can be decoupled and studied. The corresponding impact on noise and linearity is investigated. New optimization methodologies for noise and linearity at both the device and circuit level are presented. In addition, this thesis demonstrates a technique that accurately extracts ac and noise parameters of devices/circuits in the millimeter-wave range. The extraction technique supports and verifies the device/circuit noise analysis from a measurement standpoint.

Prediction of bubble formation during filling of microchambers is often critical for determining the efficacy of microfluidic devices in various applications. In this study experimental validation is performed to verify the predictions from a previously developed numerical model using lumped analyses for simulating bubble formation during the filling of microchambers. The lumped model is used to predict bubble formation in a micro-chamber as a function of the chamber geometry, fluid properties (i.e. viscosity and surface tension), surface condition (contact angle, surface roughness) and operational parameters (e.g., flow rate) as user defined inputs. Several microchambers with different geometries and surface properties were microfabricated. Experiments were performed to fill the microchambers with different liquids (e.g., water and alcohol) at various flow rates to study the conditions for bubble formation inside the microchambers. The experimental data are compared with numerical predictions to identify the limitations of the numerical model. Also, the comparison of the experimental data with the numerical results provides additional insight into the physics of the micro/nano-scale flow phenomena. The results indicate that contact angle plays a significant role on properties of fluids confined within small geometries, such as in microfluidic devices.

Measurement-based behavioral electromagnetic interference (EMI) models have been shown earlier to accurately capture the EMI behavior of switched power converters. These models are compact, linear, and run in frequency domain, enabling faster and more stable simulations compared to the detailed lumped circuit models. So far, the behavioral EMI modeling techniques are developed and applied to the converter's input side only. The resulting models are therefore referred to as "terminated EMI models". Under the condition that the output side of the converter remains fixed, these models can predict the input side EMI for any change in the impedance of the input side network. However, any change at the output side would require re-extraction of the behavioral model. Thus the terminated EMI models are incapable of predicting the change in the input side EMI due to changes at the output side of the converter or vice versa. The above mentioned limitation has been overcome by an "un-terminated EMI model" proposed in this dissertation. Un-terminated EMI models are developed here to predict both the common-mode (CM) and the differential (DM) noise currents at the input and the output sides of a motor-drive system. The modeling procedure itself has been simplified and now requires fewer measurements and results in less noise in the identified model parameters. Both CM and DM models are then combined to predict the total noise in the motor drive system. All models are validated by experiments and their limitations identified. A significant portion of this dissertation is then devoted to the application of behavioral EMI models in the design of EMI filters. Comprehensive design procedures are developed for both DM and CM filters in a motor-drive system. The filters designed using the proposed methods are experimentally shown to satisfy the DO-160 conducted emissions standards. The dissertation ends with a summary of contributions, limitations, and some future research directions. / Ph. D.

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No description available.

No description available.

Mental illness among homeless populations is a significant public health issue. Community-based programs that assist the homeless are most often developed to meet local housing needs, not the needs of mental health populations. Transitional housing, a model frequently utilized to address homelessness in communities, provides program-based housing with supportive services. The purpose of this study was to examine the associations between participant- and program-level factors on the utilization of community-based transitional housing by homeless veterans diagnosed with a mental illness. The study tested a revised framework of the behavioral model of utilization for vulnerable populations theory. The sample was comprised of male homeless veterans diagnosed with a mental illness who participated in community-based transitional housing programs in 2004 and 2005 (n = 2,502). Data were collected on 288 programs throughout the United States, operated by local nonprofit or local government agencies and monitored by the U.S. Department of Veterans Affairs under the Homeless Providers Grant and Per Diem Programs. Success was defined as either completion of a course of treatment as determined by a master's prepared clinician, or if housing was obtained upon discharge, as reported by the participant. Initial bivariate results indicated that both demographic and situational variables predicted success in transitional housing. However, upon further statistical analyses, limited predictors were revealed. Participants were more likely to be successful if they were white, reported combat experience, were interested in the program prior to admission, and were enrolled in cognitive behavioral models. Participants were more likely to be housed upon discharge if they were white, received some type of public support, were homeless less than 30 days before admission, and showed interest in the program at the time of the initial interview. Participants were less likely to be successful if they were diagnosed as schizophrenic. There was an indication that participants enrolled in programs designated as faith-based were less likely to be housed than those enrolled in secular programs. No statistically significant associations were found between the level of services offered in the transitional housing programs with either successful completion or participants' housing upon discharge.

Adverse childhood experiences (ACEs) are linked to increased mental health problems in children, but their association with mental health services utilization is not well known. This secondary analysis used 2016 National Survey of Children’s Health data from two samples: children aged 6-17-years-old with a mental or behavioral condition in need of treatment or counseling (N = 5,723); and a subsample of children who experienced at least one ACE (n = 3,812). Multiple logistic regression and latent class analysis (LCA) were performed to examine the association between ACEs and mental health services utilization. Multiple logistic regressions also examined the associations of parent/caretaker vulnerability, school-system, and medical-system factors on mental health services utilization for children with ACEs using the Gelberg-Andersen Behavioral Model for Vulnerable Populations and a Systems of Care approach as the framework for model building. Children with increased ACE scores did not have higher odds of utilizing mental health services compared to children at lower levels of or no ACEs. For children who experienced adversity, increased parent/caretaker vulnerability was associated with lower odds and the current receipt of special education services with increased odds of mental health services utilization in adjusted models. Strengths of this study included the large dataset and generalizability to the U.S. population. There were limitations to the measurement of ACEs and other key variables. The current study identified children who experienced adversity as an underserviced population for mental health services.

Mental disorders among adolescents are on the rise and are among the most common chronic conditions, constituting a national epidemic. Prevalence rates indicate that 20–40% of the adolescent population in the U.S. suffers from emotional or behavioral difficulties significant enough to lead to functional impairment. Further, estimates suggest that more than 20% of the adolescent population has a diagnosable mental disorder, which significantly impacts their functioning substantially at school, at home and in their communities. Despite national agendas to address the mental health needs of adolescents, studies suggest that approximately 50 percent of adolescents with mental health needs do not seek mental health services. Framed within the Andersen Behavioral Health Model, the objective of this research was to identify and describe the factors that contribute to the under-utilization of mental health services among adolescents. A qualitative meta-synthesis of the literature was conducted to identify the contributing factors. The goal of a meta-synthesis is to provide a greater depth of knowledge and a more extensive understanding of both the theory and the phenomena being studied. A meta-synthesis of 12 qualitative studies was conducted. Focusing on qualitative studies provides a deeper understanding of the contextual issues involved in the utilization of mental health services for adolescents. Findings from this study suggest that need and enabling factors are important drivers of mental health service use and adolescents’ and parents’ perceptions of services, providers, and sigma are particularly important to determining use of services. The study also identified that opinions of families and peers can influence whether or not an adolescent will remain in treatment. It is critical to increase understanding of the role social networks as many adolescents suggest their network may provide some form of informal support. It may be beneficial to focus specifically on the provision of peer-delivered support services for adolescents as well as develop and implement strategies aimed at improving perceptions around mental health. Adolescence is a critical point in time when social and emotional development are encouraged; if mental health needs are not addressed during this developmental stage, many adolescents will potentially experience more severe disorders in adulthood.