Study of Methods for Microfabrication of Fused Silica Planar CVGs and Direct Angle Measurements

This Ph.D. dissertation focuses on developing microfabrication processes to realize planar Fused Silica (FS) Micro-Electro-Mechanical System (MEMS) technology for the implementation of Micro-Rate-Integrating Gyroscopes (MRIG). Additionally, it includes a study on identifying error sources in Coriolis Vibratory Gyroscopes (CVG) with Whole-Angle (WA) mode of operation. The FS planar MRIG is envisioned to provide navigational-grade noise performance, unprecedented dynamic range, and thermal stability at the Size, Weight, Power, and Cost (SWaP-C) factor of today's commercially-available silicon micro-scale gyroscopes. As part of this work, we developed the Fused silica-On-Silicon (FOS) process for batch fabrication of resonators from low-loss amorphous FS material based on conventional microfabrication techniques. As the core step of the fabrication process for plasma etching of FS, we identified optimal parameters to achieve a 7:1 aspect ratio of etching with near-vertical sidewalls. For demonstration, a Toroidal Ring Gyroscope (TRG) architecture was fabricated through the developed process. With the metal-coated FS-TRG devices, we demonstrated resonator functionality and measured a quality factor on the order of 539,000 and a frequency split in the operational modes as low as 7~Hz. We also introduced a digital manufacturing process, which utilizes Femtosecond Laser-Induced Chemical Etching (FLICE) to fabricate stand-alone FS MEMS vibratory structures for the first time. Through process optimization, we demonstrated that FLICE is an enabling technology for patterning micro-channels with an aspect ratio of 55:1 and higher, ideal for fabricating MEMS resonators with ultrahigh capacitive transduction. By employing FLICE as part of a 3-step process, we fabricated Disk Resonator Gyroscopes (DRG) from a single-layer FS material. We demonstrated resonator functionality and measured the frequency split as low as 54.7~Hz and the quality factor as high as 614,000. To the best of our knowledge, the quality factors demonstrated as part of this dissertation are the highest quality factors reported for a planar FS resonator in the kilohertz frequency range. We developed a mathematical electro-mechanical model of WA operation, through which we simulated and characterized the effect of mechanical imperfections of the structure and imperfections in the WA control electronics on precession of oscillations. We demonstrated that mechanical imperfections in MRIG, including anisodamping and anisoelasticity, limit the resolution of angle measurements by introducing angle-dependent bias errors and angular drift. Imperfections in the control electronics, including phase errors, asymmetries in motion actuation and detection gains, were shown to adversely affect the outcome of the WA control loops and cause interference on the precession. We verified the simulation results experimentally by implementing the WA control with an FPGA/DSP-based platform and applying it to a planar silicon MRIG as the testbed. Finally, we identified a mismatch in MRIG's Temperature Coefficients of Frequency (TCF) as the primary mechanism causing Angular Gain Temperature Sensitivity (AGTS) and angular drift in the WA mode of operation. We provided mitigation strategies to significantly reduce AGTS, despite the TCF-mismatch in the mechanical sensing element. We demonstrated angular gain stability better than 223~ppb (equivalent to a rate-bias-stability better than 0.2~deg/hr), which would be critical for high accuracy angle measurements in prolonged operations

Reliable and Energy Efficient Battery-Powered Cyber-Physical Systems

Cyber-Physical Systems (CPS) were presented as a solution to multidisciplinary integration and control in embedded systems. They provide seamless interactions between cyber and physical domains, enabling more intelligent and complicated control applications. However, CPS face the challenges of reliability and energy efficiency since they mainly rely on batteries for power supply. We investigate these issues with Electric Vehicles (EV) which are common battery-powered CPS. EV were introduced as a mean of transportation to address environmental problems like air and noise pollution. However, their stringent design constraints, especially on battery packs, create challenges of limited driving range and battery lifetime for daily drivers and manufacturers. Design automation community has been addressing these by developing more efficient and dependable devices and control methodologies. Our contributions in this thesis will embrace: 1) novel machine learning and physics-based modeling techniques to capture CPS dynamics more accurately; 2) unique optimization problem formulations to make optimal control decisions; and 3) intelligent control methodologies that leverage the modeling and interaction within CPS to achieve reliable and efficient operation. These contributions are applied to the systems in EV such as navigation system, climate control, and battery management system. Our objectives are to further extend the EV driving range and prolong the battery lifetime while maintaining similar driving experience and comfort for passengers

Design Space Exploration and Energy Management in Residential Microgrids

Microgrid has been shown to be profitable, reliable, and efficient for military, commercial, and university‐like installations. However, until now, there has been no study to show how and when a residential microgrid may be profitable. Therefore, in this thesis, we present a design space exploration methodology of the microgrid by modeling all the energy resources at the residential level and conducting numerous simulations with various parameters. Moreover, a set of rules are defined to make the stakeholders in the microgrid profitable. Also, by analyzing the number of houses in the microgrid, we observe that the number of years it takes to return the capital costs invested in the microgrid may become adequately short for a certain range of the number of houses. For instance, if the aggregator owns the renewable energy resources, e.g., solar panels, it may profit in less than five years when 500 houses participate in the microgrid where each house owns 500 sf solar panels. On the other hand, if the prosumers own the renewable energy resources, e.g., solar panels, the aggregator may profit in about a year. Typically, for an apartment‐block type housing area in U.S. there are more than 1000 houses, therefore the aggregator profitability may improve furthermore

Reliable and Energy Efficient Battery-Powered Cyber-Physical Systems

Cyber-Physical Systems (CPS) were presented as a solution to multidisciplinary integration and control in embedded systems. They provide seamless interactions between cyber and physical domains, enabling more intelligent and complicated control applications. However, CPS face the challenges of reliability and energy efficiency since they mainly rely on batteries for power supply. We investigate these issues with Electric Vehicles (EV) which are common battery-powered CPS. EV were introduced as a mean of transportation to address environmental problems like air and noise pollution. However, their stringent design constraints, especially on battery packs, create challenges of limited driving range and battery lifetime for daily drivers and manufacturers. Design automation community has been addressing these by developing more efficient and dependable devices and control methodologies. Our contributions in this thesis will embrace: 1) novel machine learning and physics-based modeling techniques to capture CPS dynamics more accurately; 2) unique optimization problem formulations to make optimal control decisions; and 3) intelligent control methodologies that leverage the modeling and interaction within CPS to achieve reliable and efficient operation. These contributions are applied to the systems in EV such as navigation system, climate control, and battery management system. Our objectives are to further extend the EV driving range and prolong the battery lifetime while maintaining similar driving experience and comfort for passengers

Compartmentalisation-based design automation method for power grid

Power grid design and maintenance are conducted to solve the problems caused by load growth over time and to stay within the constraints of voltage drop, power factor, etc. Typically, solutions to these problems are optimised individually. Considering multiple problems simultaneously and applying different solutions require vast design space exploration. This exclusively needs advanced algorithms and complex global optimisation methods which are not easily-applicable in different scenarios. In the state-of-the-art methods, for solving multiple problems simultaneously, these individually optimised solutions are applied sequentially to the power grid. In this so-called uncoordinated method, the final solution may not be optimal solution considering all the variables, since it is considering the overlapping effect of the solutions on the power grid. To validate the compartmentalisation method, a detailed distribution grid has been modeled. After analysing the possible solutions and optimisation, power loss was reduced 45% and total cost decreased by 71%, compared to the uncoordinated method