High-accuracy Motion Estimation for MEMS Devices with Capacitive Sensors

With the development of micro-electro-mechanical system (MEMS) technologies, emerging MEMS applications such as in-situ MEMS IMU calibration, medical imaging via endomicroscopy, and feedback control for nano-positioning and laser scanning impose needs for especially accurate measurements of motion using on-chip sensors. Due to their advantages of simple fabrication and integration within system level architectures, capacitive sensors are a primary choice for motion tracking in those applications. However, challenges arise as often the capacitive sensing scheme in those applications is unconventional due to the nature of the application and/or the design and fabrication restrictions imposed, and MEMS sensors are traditionally susceptible to accuracy errors, as from nonlinear sensor behavior, gain and bias drift, feedthrough disturbances, etc. Those challenges prevent traditional sensing and estimation techniques from fulfilling the accuracy requirements of the candidate applications. The goal of this dissertation is to provide a framework for such MEMS devices to achieve high-accuracy motion estimation, and specifically to focus on innovative sensing and estimation techniques that leverage unconventional capacitive sensing schemes to improve estimation accuracy. Several research studies with this specific aim have been conducted, and the methodologies, results and findings are presented in the context of three applications. The general procedure of the study includes proposing and devising the capacitive sensing scheme, deriving a sensor model based on first principles of capacitor configuration and sensing circuit, analyzing the sensor’s characteristics in simulation with tuning of key parameters, conducting experimental investigations by constructing testbeds and identifying actuation and sensing models, formulating estimation schemes is to include identified actuation dynamics and sensor models, and validating the estimation schemes and evaluating their performance against ground truth measurements. The studies show that the proposed techniques are valid and effective, as the estimation schemes adopted either fulfill the requirements imposed or improve the overall estimation performance. Highlighted results presented in this dissertation include a scale factor calibration accuracy of 286 ppm for a MEMS gyroscope (Chapter 3), an improvement of 15.1% of angular displacement estimation accuracy by adopting a threshold sensing technique for a scanning micro-mirror (Chapter 4), and a phase shift prediction error of 0.39 degree for a electrostatic micro-scanner using shared electrodes for actuation and sensing (Chapter 5).PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147568/1/davidsky_1.pd

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

Polycrystalline Silicon Capacitive MEMS Strain Sensor for Structural Health Monitoring of Wind Turbines

Wind energy is a fast-growing sustainable energy technology and driven by the need for more efficient energy harvesting, size of the wind turbines has increased over the years for both off-shore and land-based installations. Therefore, structural health monitoring and maintenance of such turbine structures have become critical and challenging. In order to keep the number of physical inspections to minimum without increasing the risk of structural failure, a precise and reliable remote monitoring system for damage identification is necessary. Condition-based maintenance which significantly improves safety compared to periodic visual inspections, necessitates a method to determine the condition of machines while in operation and involves the observation of the system by sampling dynamic response measurements from a group of sensors and the analysis of the data to determine the current state of system health. This goal is being pursued in this thesis through the development of reliable sensors, and reliable damage detection algorithms. Blade strain is the most important quantities to judge the health of wind turbine structure. Sensing high stress fields or early detection of cracks in blades bring safety and saving in rehabilitation costs. Therefore, high performance strain measurement system, consisting of sensors and interface electronics, is highly desirable and the best choice. It has been revealed that the conventional strain gauge techniques exhibit significant errors and uncertainties when applied to composite materials of wind turbine blades. Micro-electro-mechanical system (MEMS) based sensors are very attractive among other sensing techniques owing to high sensitivity, low noise, better scaling characteristics, low cost and higher potential for integration with low power CMOS circuits. MEMS sensors that are fabricated on a chip can be either bonded to the surface of wind turbine blade or embedded into the fiber reinforced composite. Therefore, MEMS technology is selected to fabricate the strain sensor in this work. Two new sensor structures that can be used for strain measurement are designed. While the proposed sensors focus on high sensitivity, they are based on simple operating principle of comb-drive differential variable capacitances and chevron displacement amplification. Device performances are validated both by analytical solutions and finite element method simulations. The transmission of strain fields in adhesively bonded strain sensors is also studied. In strain sensors that are attached to host structures using adhesive layers such as epoxy, complete strain transfer to the sensor is hindered due to the influence of the adhesive layer on the transfer. An analytical model, validated by finite element method simulation, to provide insight and accurate formulation for strain transfer mechanism for bonded sensors is developed. The model is capable of predicting the strain transmission ratio through a sensor gauge factor, and it clearly establishes the effects of the flexibility, length, and thickness of the adhesive layer and sensor substrate. Several fabrication steps were required to realize the MEMS capacitive strain sensor in our lab. Polycrystalline silicon is selected as the structural layer and silicon nitride as the sacrificial layer. Polysilicon is deposited using LPCVD and SiN is deposited by PECVD in our lab. A comprehensive material study of silicon nitride and polycrystalline silicon layers is therefore performed. The whole fabrication process involves deposition, etching, and photolithography of five material layers. Although this process is developed to realize the MEMS strain sensors, it is also able to fabricate other designs of surface micromachining structures as well. The fabricated MEMS capacitive strain sensors are tested on a test fixture setup. The measurement setup is created under the probe station by using a cantilever beam fixed on one side and free on other side where a micrometer applies accurate displacement. The displacement creates bending stress on the beam which transfers to the MEMS sensor through the adhesive bond. Measurement results are in a good match with the simulation results. Finally, a real-time non-destructive health monitoring technique based on multi-sensor data fusion is proposed. The objective is to evaluate the feasibility of the proposed method to identify and localize damages in wind turbine blades. The structural properties of turbine blade before and after damage are investigated and based on the obtained results, it is shown that information from smart sensors, measuring strains and vibrations, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine the diagnostic tools to improve the detection system with providing a more robust reading and fewer false alarms

EUROSENSORS XVII : book of abstracts

Fundação Calouste Gulbenkien (FCG).Fundação para a Ciência e a Tecnologia (FCT)