Matching Items (2)
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- All Subjects: Mathematical Modeling
- Creators: Forzani, Erica S
Description
A low temperature amorphous oxide thin film transistor (TFT) and amorphous silicon PIN diode backplane technology for large area flexible digital x-ray detectors has been developed to create 7.9-in. diagonal backplanes. The critical steps in the evolution of the backplane process include the qualification and optimization of the low temperature (200 °C) metal oxide TFT and a-Si PIN photodiode process, the stability of the devices under forward and reverse bias stress, the transfer of the process to flexible plastic substrates, and the fabrication and assembly of the flexible detectors.
Mixed oxide semiconductor TFTs on flexible plastic substrates suffer from performance and stability issues related to the maximum processing temperature limitation of the polymer. A novel device architecture based upon a dual active layer improves both the performance and stability. Devices are directly fabricated below 200 ºC on a polyethylene naphthalate (PEN) substrate using mixed metal oxides of either zinc indium oxide (ZIO) or indium gallium zinc oxide (IGZO) as the active semiconductor. The dual active layer architecture allows for adjustment to the saturation mobility and threshold voltage stability without the requirement of high temperature annealing, which is not compatible with flexible plastic substrates like PEN. The device performance and stability is strongly dependent upon the composition of the mixed metal oxide; this dependency provides a simple route to improving the threshold voltage stability and drive performance. By switching from a single to a dual active layer, the saturation mobility increases from 1.2 cm2/V-s to 18.0 cm2/V-s, while the rate of the threshold voltage shift decreases by an order of magnitude. This approach could assist in enabling the production of devices on flexible substrates using amorphous oxide semiconductors.
Low temperature (200°C) processed amorphous silicon photodiodes were developed successfully by balancing the tradeoffs between low temperature and low stress (less than -70 MPa compressive) and device performance. Devices with a dark current of less than 1.0 pA/mm2 and a quantum efficiency of 68% have been demonstrated. Alternative processing techniques, such as pixelating the PIN diode and using organic photodiodes have also been explored for applications where extreme flexibility is desired.
Mixed oxide semiconductor TFTs on flexible plastic substrates suffer from performance and stability issues related to the maximum processing temperature limitation of the polymer. A novel device architecture based upon a dual active layer improves both the performance and stability. Devices are directly fabricated below 200 ºC on a polyethylene naphthalate (PEN) substrate using mixed metal oxides of either zinc indium oxide (ZIO) or indium gallium zinc oxide (IGZO) as the active semiconductor. The dual active layer architecture allows for adjustment to the saturation mobility and threshold voltage stability without the requirement of high temperature annealing, which is not compatible with flexible plastic substrates like PEN. The device performance and stability is strongly dependent upon the composition of the mixed metal oxide; this dependency provides a simple route to improving the threshold voltage stability and drive performance. By switching from a single to a dual active layer, the saturation mobility increases from 1.2 cm2/V-s to 18.0 cm2/V-s, while the rate of the threshold voltage shift decreases by an order of magnitude. This approach could assist in enabling the production of devices on flexible substrates using amorphous oxide semiconductors.
Low temperature (200°C) processed amorphous silicon photodiodes were developed successfully by balancing the tradeoffs between low temperature and low stress (less than -70 MPa compressive) and device performance. Devices with a dark current of less than 1.0 pA/mm2 and a quantum efficiency of 68% have been demonstrated. Alternative processing techniques, such as pixelating the PIN diode and using organic photodiodes have also been explored for applications where extreme flexibility is desired.
ContributorsMarrs, Michael (Author) / Raupp, Gregory B (Thesis advisor) / Allee, David R. (Committee member) / Dai, Lenore L (Committee member) / Forzani, Erica S (Committee member) / Bawolek, Edward J (Committee member) / Arizona State University (Publisher)
Created2016
Description
Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed.
ContributorsFreigoun, Mohammad T (Author) / Raupp, Gregory B (Thesis advisor) / Tsakalis, Konstantinos S (Thesis advisor) / Spanias, Andreas S (Committee member) / Forzani, Erica S (Committee member) / Muhich, Christopher L (Committee member) / Arizona State University (Publisher)
Created2021