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- All Subjects: pmc
- Creators: Allee, David R.
- Creators: Roedel, Ronald
- Resource Type: Text
Description
Programmable Metallization Cell (PMC) is a technology platform which utilizes mass transport in solid or liquid electrolyte coupled with electrochemical (redox) reactions to form or remove nanoscale metallic electrodeposits on or in the electrolyte. The ability to redistribute metal mass and form metallic nanostructure in or on a structure in situ, via the application of a bias on laterally placed electrodes, creates a large number of promising applications. A novel PMC-based lateral microwave switch was fabricated and characterized for use in microwave systems. It has demonstrated low insertion loss, high isolation, low voltage operation, low power and low energy consumption, and excellent linearity. Due to its non-volatile nature the switch operates with fewer biases and its simple planar geometry makes possible innovative device structures which can be potentially integrated into microwave power distribution circuits. PMC technology is also used to develop lateral dendritic metal electrodes. A lateral metallic dendritic network can be grown in a solid electrolyte (GeSe) or electrodeposited on SiO2 or Si using a water-mediated method. These dendritic electrodes grown in a solid electrolyte (GeSe) can be used to lower resistances for applications like self-healing interconnects despite its relatively low light transparency; while the dendritic electrodes grown using water-mediated method can be potentially integrated into solar cell applications, like replacing conventional Ag screen-printed top electrodes as they not only reduce resistances but also are highly transparent. This research effort also laid a solid foundation for developing dendritic plasmonic structures. A PMC-based lateral dendritic plasmonic structure is a device that has metallic dendritic networks grown electrochemically on SiO2 with a thin layer of surface metal nanoparticles in liquid electrolyte. These structures increase the distribution of particle sizes by connecting pre-deposited Ag nanoparticles into fractal structures and result in three significant effects, resonance red-shift, resonance broadening and resonance enhancement, on surface plasmon resonance for light trapping simultaneously, which can potentially enhance thin film solar cells' performance at longer wavelengths.
ContributorsRen, Minghan (Author) / Kozicki, Michael (Thesis advisor) / Schroder, Dieter (Committee member) / Roedel, Ronald (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2011
Description
The formation of dendrites in materials is usually seen as a failure-inducing defect in devices. Naturally, most research views dendrites as a problem needing a solution while focusing on process control techniques and post-mortem analysis of various stress patterns with the ultimate goal of total suppression of the structures. However, programmable metallization cell (PMC) technology embraces dendrite formation in chalcogenide glasses by utilizing the nascent conductive filaments as its core operative element. Furthermore, exciting More-than-Moore capabilities in the realms of device watermarking and hardware encryption schema are made possible by the random nature of dendritic branch growth. While dendritic structures have been observed and are well-documented in solid state materials, there is still no satisfactory theoretical model that can provide insight and a better understanding of how dendrites form. Ultimately, what is desired is the capability to predict the final structure of the conductive filament in a PMC device so that exciting new applications can be developed with PMC technology.
This thesis details the results of an effort to create a first-principles MATLAB simulation model that uses configurable physical parameters to generate images of dendritic structures. Generated images are compared against real-world samples. While growth has a significant random component, there are several reliable characteristics that form under similar parameter sets that can be monitored such as the relative length of major dendrite arms, common branching angles, and overall growth directionality.
The first simulation model that was constructed takes a Newtonian perspective of the problem and is implemented using the Euler numerical method. This model has several shortcomings stemming majorly from the simplistic treatment of the problem, but is highly performant. The model is then revised to use the Verlet numerical method, which increases the simulation accuracy, but still does not fully resolve the issues with the theoretical background. The final simulation model returns to the Euler method, but is a stochastic model based on Mott-Gurney’s ion hopping theory applied to solids. The results from this model are seen to match real samples the closest of all simulations.
This thesis details the results of an effort to create a first-principles MATLAB simulation model that uses configurable physical parameters to generate images of dendritic structures. Generated images are compared against real-world samples. While growth has a significant random component, there are several reliable characteristics that form under similar parameter sets that can be monitored such as the relative length of major dendrite arms, common branching angles, and overall growth directionality.
The first simulation model that was constructed takes a Newtonian perspective of the problem and is implemented using the Euler numerical method. This model has several shortcomings stemming majorly from the simplistic treatment of the problem, but is highly performant. The model is then revised to use the Verlet numerical method, which increases the simulation accuracy, but still does not fully resolve the issues with the theoretical background. The final simulation model returns to the Euler method, but is a stochastic model based on Mott-Gurney’s ion hopping theory applied to solids. The results from this model are seen to match real samples the closest of all simulations.
ContributorsFoss, Ryan (Author) / Kozicki, Michael N (Thesis advisor) / Barnaby, Hugh (Committee member) / Allee, David R. (Committee member) / Arizona State University (Publisher)
Created2016