On variability and reliability of poly-Si thin-film transistors

In contrast to conventional bulk-silicon technology, polysilicon (poly-Si) thin-film transistors (TFTs) can be implanted in flexible substrate and can have low process temperature. These attributes make poly-Si TFT technology more attractive for new applications, such as flexible displays, biosensors, and smart clothing. However, due to the random nature of grain boundaries (GBs) in poly-Si film and self-heating enhanced negative bias temperature instability (NBTI), the variability and reliability of poly-Si TFTs are the main obstacles that impede the application of poly-Si TFTs in high-performance circuits. The primary focus of this dissertation is to develop new design methodologies and modeling techniques for facilitating new applications of poly-Si TFT technology. In order to do that, a physical model is first presented to characterize the GB-induced transistor threshold voltage (V th)variations considering not only the number but also the position and orientation of each GB in 3-D space. The fast computation time of the proposed model makes it suitable for evaluation of GB-induced transistor Vthvariation in the early design phase. Furthermore, a self-consistent electro-thermal model that considers the effects of device geometry, substrate material, and stress conditions on NBTI is proposed. With the proposed modeling methodology, the significant impacts of device geometry, substrate, and supply voltage on NBTI in poly-Si TFTs are shown. From a circuit design perspective, a voltage programming pixel circuit is developed for active-matrix organic light emitting diode (AMOLED) displays for compensating the shift of Vth and mobility in driver TFTs as well as compensating the supply voltage degradation. In addition, a self-repair design methodology is proposed to compensate the GB-induced variations for liquid crystal displays (LCDs) and AMOLED displays. Based on the simulation results, the proposed circuit can decrease the required supply voltage by 20% without performance and yield degradation. In the final section of this dissertation, an optimization methodology for circuit-level reliability tests is explored. To effectively predict circuit lifetime, accelerated aging (i.e. elevated voltage and temperature) is commonly applied in circuit-level reliability tests, such as constant voltage stress (CVS) and ramp voltage stress (RVS) tests. However, due to the accelerated aging, shifting of dominant degradation mechanism might occur leading to the wrong lifetime prediction. To get around this issue, we proposed a technique to determine the proper stress range for accelerated aging tests

Nonlocal priors for Bayesian variable selection in generalized linear models and generalized linear mixed models and their applications in biology data

Abstract from short.pdf file.Dissertation supervisors: Dr. Marco A. R. Ferreira and Dr. Tieming Ji.Includes vita.A crucial problem in building a generalized linear model (GLM) or a generalized linear mixed model (GLMM) is to identify which subset of predictors should be included into the model. Hence, the main thrust of this dissertation is aimed to discuss and showcase our promising Bayesian methods that circumvent this problem in both GLMs and GLMMs. In the first part of the dissertation, we study the hyper-g prior based Bayesian variable selection procedure for generalized linear models. In the second part of the dissertation, we propose two novel scale mixtures of nonlocal priors (SMNP) for variable selection in GLMs. In the last part of the dissertation, we develop novel nonlocal prior for variable selection in generalized linear mixed models (GLMM) and apply the proposed nonlocal prior and its inference procedure for the whole genome allelic imbalance detection.Includes bibliographical references (pages 102-110)

Unraveling US Newspapers’ Digital and Print Subscriptions in the Context of Price, 2016–2022

Despite industry-wide efforts in digitally transforming news organizations, research showed that most newspapers’ legacy products still outperformed the same newspaper’s digital offerings in terms of engagement, circulation, readership, pricing, advertising, and subscription revenue—all by a wide margin. But Covid-19 created an unprecedented scenario where the need for instant, local news updates, the fear of contacting anything tangible, and financial stress may have changed consumer behavior. To assess the state of the newspaper industry, this study analyzes short-term and long-term trends in US newspapers’ digital and print circulation before and during the pandemic. The analysis considered price, an important factor often neglected in discussions about newspaper demand. Utilizing rich industry data, this study analyzed 18 US metro daily newspapers’ circulation trends during 2016–2022. The results revealed that digital circulation increased rapidly after the onset of Covid-19 but subsequently decreased after reaching the peak in Q3 2021. Print circulation continued its rapid decline since 2016, accompanied by continuous, substantial price hikes for print subscriptions—a typical print subscription now costs over $1,000 a year. Despite circulation declines, the print edition remains the core product, with more subscribers paying far more than digital subscribers. Because of the immense price gap (6 to 1), the seemingly promising increase in digital subscriptions during Covid-19 could not generate nearly as much revenue to cover the loss on the print side, resulting in a substantial loss in total subscription revenue. The state of the US newspaper industry needs immediate attention.Journalism and Medi

Addiction and Substance Abuse among Nevada Youths

Adolescence is a critical stage for substance abuse initiation, as well as for substance abuse prevention, intervention, and treatment. This report presents tobacco product use, alcohol use, and illicit drug use rates of Nevada’s youths (aged 12-17) compared with youths nationwide as a whole. Most data were retrieved from the Substance Abuse and Mental Health Services Administration (SAMHSA), including the 2008--2009 National Survey on Drug Use and Health (NSDUH) data and Treatment Episode Data Set (TEDS), supplemented with the Centers for Disease Control and Prevention (CDC)’s 2009 Youth Risk Behavior Survey (YRBS) data. Substate data were taken from SAMHSA’s 2006-2008 NSDUH data, the most updated substate data available so far. The 2010 NSDUH state-level data won’t be released by SAMHSA until 2012