Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 157 pages. Includes vita. Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2004. / Includes bibliographical references (p. 71-72).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 59-61). Also available in electronic version. Access restricted to campus users.

Bandgap reference circuits are used in a host of analog, digital, and mixed-signal systems to establish an accurate voltage reference for the entire IC. The most used CMOS implementation for voltage references is the bandgap circuit due to its high predictability, and low dependence of the supply voltage and temperature of operation. This work studies a CMOS implementation of a resistor-less bandgap reference, which consumes low power. The most relevant and traditional approaches usually employed to implement bandgap voltage references are investigated. The impact of process, power-supply, load and temperature variations has been analyzed and simulated. The functionality of critical components of the circuit has been verified through chip implementation. / text

Emerging transparent semiconducting oxide (TSO) materials have achieved their initial commercial success in the display industry. Due to the advanced electrical performance, TSOs have been adopted either to improve the performance of traditional displays or to demonstrate the novel transparent and flexible displays. However, due to the lack of feasible p-type TSOs, the applications of TSOs is limited to unipolar (n-type TSOs) based devices. Compared with the prosperous n-type TSOs, the performance of p-type counterparts is lag behind. However, after years of discovery, several p-type TSOs are confirmed with promising performance, for example, tin monoxide (SnO). By using p-type SnO, excellent transistor field-effect mobility of 6.7 cm2 V-1 s-1 has been achieved. Motivated by this encouraging performance, this dissertation is devoted to further evaluate the feasibility of integrating p-type SnO in p-n junctions and complementary metal oxide semiconductor (CMOS) devices. CMOS inverters are fabricated using p-type SnO and in-situ formed n-type tin dioxide (SnO2). The semiconductors are simultaneously sputtered, which simplifies the process of CMOS inverters. The in-situ formation of SnO2 phase is achieved by selectively sputtering additional capping layer, which serves as oxygen source and helps to balance the process temperature for both types of semiconductors. Oxides based p-n junctions are demonstrated between p-type SnO and n-type SnO2 by magnetron sputtering method. Diode operating ideality factor of 3.4 and rectification ratio of 103 are achieved. A large temperature induced knee voltage shift of 20 mV oC-1 is observed, and explained by the large band gap and shallow states in SnO, which allows minor adjustment of band structure in response to the temperature change. Finally, p-type SnO is used to demonstrating the hybrid van der Waals heterojunctions (vdWHs) with two-dimensional molybdenum disulfide (2D MoS2) by mechanical exfoliation. The hybrid vdWHs show excellent rectifying performance. Due to the ultra-thin nature of MoS2, the operation of hybrid vdWHs is gate-tunable, and we further discover such gate-tunability depends on the layer number of MoS2, i.e., the screening effect. The detailed study in such hybrid vdWHs provides valuable information for understanding the switching performance of junctions contain 2D materials.

Dark current is an unwanted source of noise in images produced by digital imagers, the de facto standard of imaging. The two most common types of digital imager architectures, Charged-Coupled Devices (CCDs) and Complementary Metal-Oxide-Semiconductor (CMOS), are both prone to this noise source. To accurately reflect the information from light signals this noise must be removed. This practice is especially vital for scientific purposes such as in astronomical observations. Presented in this dissertation are characterizations of dark current sources that present complications to the traditional methods of correction. In particular, it is observed that pixels in both CCDs and CMOS image sensors produce dark current that is affected by the presence of pre-illuminating the sensor and that these same pixels produce a nonlinear dark current with respect to exposure time. These two characteristics are not conventionally accounted for as it is assumed that the dark current produced will be unaffected by charge accumulated from either illumination or the dark current itself. Additionally, a model reproducing these dark current characteristics is presented. The model incorporates a moving edge of the depletion region, where charge is accumulated, as well as fixed recombination-generation locations. Recombination-generation sites in the form of heavy metal impurities, or lattice defects, are commonly the source of dark current especially in the highest producing pixels, commonly called "hot pixels." The model predicts that pixels with recombination-generation sites near the edge of an empty depletion region will produce less dark current after accumulation of charge, accurately modeling the behavior observed from empirical sources. Finally, it is shown that activation energy calculations will produce inconsistent results for pixels with the presence of recombination-generation sites near the edge of a moving depletion region. Activation energies, an energy associated with the temperature dependence of dark current, are often calculated to characterize aspects of the dark current including types of impurities and sources of dark current. The model is shown to generate data, including changing activation energy values, that correspond with changing activation energy calculations in those pixels observed to be affected by pre-illumination and that produce inconsistent dark current over long exposure times. Rather than only being a complication to dark current correction, the presence of such pixels, and the model explaining their behavior, presents an opportunity to obtain information, such as the depth of these recombination-generation sites, which will aid in refining manufacturing processes for digital imagers.

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Energetic particles from cosmic ray or terrestrial sources can strike sensitive areas of CMOS devices and cause soft errors. Understanding the effects of such interactions is crucial as the device technology advances, and chip reliability has become more important than ever. Particle accelerator testing has been the standard method to characterize the sensitivity of chips to single event upsets (SEUs). However, because of their costs and availability limitations, other techniques have been explored. Pulsed laser has been a successful tool for characterization of SEU behavior, but to this day, laser has not been recognized as a comparable method to beam testing. In this thesis, I propose a methodology of correlating laser soft error rate (SER) to particle beam gathered data. Additionally, results are presented showing a temperature dependence of SER and the "neighbor effect" phenomenon where due to the close proximity of devices a "weakening effect" in the ON state can be observed.

Legislative reforms aimed at slowing growth of US healthcare costs are focused on achieving greater value, defined specifically as health outcomes achieved per dollar spent. To increase value while payments are diminishing and tied to individual outcomes, healthcare must improve at predicting risks and outcomes. One way to improve predictions is through better modeling methods. Current models are predominantly based on logistic regression (LR). This project applied Reconstructability Analysis (RA) to data on hip and knee replacement surgery, and considered whether RA could create useful models of outcomes, and whether these models could produce predictions complimentary to or even stronger than LR models. RA is a data mining method that searches for relations in data, especially non-linear and higher ordinality relations, by decomposing the frequency distribution of the data into projections, several of which taken together define a model, which is then assessed for statistical significance. The predictive power of the model is expressed as the percent reduction of uncertainty (Shannon entropy) of the dependent variable (the DV) gained by knowing the values of the predictive independent variables (the IVs). Results showed that LR and RA gave the same results for equivalent models, and showed that exploratory RA provided better models than LR. Sixteen RA predictive models were then generated across the four DVs: complications, skilled nursing discharge, readmissions, and total cost. While the first three DVs are nominal, RA generated continuous predictions for cost by calculating expected values. Models included novel comorbidity variables and non-hypothesized interaction terms, and often resulted in substantial reductions in uncertainty. Predictive variables consisted of both delivery system variables and binary patient comorbidity variables. Complications were predicted by the total number of patient comorbidities. Skilled nursing discharges were predicted both by patient-related factors and delivery system variables (location, surgeon volume), suggesting practice patterns influence utilization of skilled nursing facilities. Readmissions were not well predicted, suggesting the data used in this project lacks the right variables or that readmissions are simply unpredictable. Delivery system variables (surgeon, location, and surgeon volume) were found to be the predominant predictors of total cost. Risk ratios were generated as an additional measure of effect size. These risk ratios were used to classify the IV states of the models as indicating higher or lower risk of adverse outcomes. Some IV states showed nearly 25% of patients at increased risk, while other IV states showed over 75% of patients at decreased risk. In real time, such risk predictions could support clinical decision making and custom-tailored utilization of services. Future research might address the limitations of this project's data and employ additional RA techniques and training-test splits. Implementation of predictive models is also discussed, with considerations for data supply lines, maintenance of models, organizational buy-in, and the acceptance of model output by clinical teams for use in real-time clinical practice. If outcomes and risk are adequately predicted, areas for potential improvement become clearer, and focused changes can be made to drive improvements in patient care. Better predictions, such as those resulting from the RA methodology, can thus support improvement in value--better outcomes at a lower cost. As reimbursement increasingly evolves into value-based programs, understanding the outcomes achieved, and customizing patient care to reduce unnecessary costs while improving outcomes, will be an active area for clinicians, healthcare administrators, researchers, and data scientists for many years to come.