IMPROVED SENSITIVITY OF RESONANT MASS SENSOR BASED ON MICRO TILTING PLATE AND MICRO CANTILEVER

Vapor sensors have been used for many years. Their applications range from detection of toxic gases and dangerous chemicals in industrial environments, the monitoring of landmines and other explosives, to the monitoring of atmospheric conditions. Microelectrical mechanical systems (MEMS) fabrication technologies provide a way to fabricate sensitive devices. One type of MEMS vapor sensors is based on mass changing detection and the sensors have a functional chemical coating for absorbing the chemical vapor of interest. The principle of the resonant mass sensor is that the resonant frequency will experience a large change due to a small mass of gas vapor change. This thesis is trying to build analytical micro-cantilever and micro-tilting plate models, which can make optimization more efficient. Several objectives need to be accomplished: (1) Build an analytical model of MEMS resonant mass sensor based on micro-tilting plate with the effects of air damping. (2) Perform design optimization of micro-tilting plate with a hole in the center. (3) Build an analytical model of MEMS resonant mass sensor based on micro-cantilever with the effects of air damping. (4) Perform design optimization of micro-cantilever by COMSOL. Analytical models of micro-tilting plate with a hole in the center are compared with a COMSOL simulation model and show good agreement. The analytical models have been used to do design optimization that maximizes sensitivity. The micro-cantilever analytical model does not show good agreement with a COMSOL simulation model. To further investigate, the air damping pressures at several points on the micro-cantilever have been compared between analytical model and COMSOL model. The analytical model is inadequate for two reasons. First, the model’s boundary condition assumption is not realistic. Second, the deflection shape of the cantilever changes with the hole size, and the model does not account for this. Design optimization of micro-cantilever is done by COMSOL.

Fabrication and characterization of room temperature operating single electron transistors using focused ion beam technologies

The single electron transistor (SET) is a Coulomb blockade device, whose operation is based on the controlled manipulation of individual electrons. Single electron transistors show immense potential to be used in future ultra lowpower devices, high density memory and also in high precision electrometry. Most SET devices operate at cryogenic temperatures, because the charging energy is much smaller than the thermal oscillations. The room temperature operation of these devices is possible with sub- 10nm nano-islands due to the inverse dependance of charging energy on the radius of the conducting nano-island. The fabrication of sub-10nm features with existing lithographic techniques is a technological challenge. Here we present the results for the first room temperature operating SET device fabricated using Focused Ion Beam deposition technology. The SET device, incorporates an array of tungsten nano-islands with an average diameter of 8nm. The SET devices shows clear Coulomb blockade for different gate voltages at room temperature. The charging energy of the device was calculated to be 160.0 meV; the capacitance per junction was found to be 0.94 atto F; and the tunnel resistance per junction was calculated to be 1.26 G Ω. The tunnel resistance is five orders of magnitude larger than the quantum of resistance (26 k Ω) and allows for the localization of electrons on the tungsten nano-island. The lower capacitance of the device combined with the high tunnel resistance, allows for the Coulomb blockade effects observed at room temperature. Different device configurations, minimizing the total capacitance of the device have been explored. The effect of the geometry of the nano electrodes on the device characteristics has been presented. Simulated device characteristics, based on the soliton model have been discussed. The first application of SET device as a gas sensor has been demonstrated.

Implementation of porous silicon technology for a fluidic flow-through optical sensor for pH measurements

This work presents an innovative integration of sensing and nano-scaled fluidic actuation in the combination of pH sensitive optical dye immobilization with the electro-osmotic phenomena in polar solvents like water for flow-through pH measurements. These flow-through measurements are performed in a flow-through sensing device (FTSD) configuration that is designed and fabricated at MTU. A relatively novel and interesting material, through-wafer mesoporous silica substrates with pore diameters of 20 -200 nm and pore depths of 500 µm are fabricated and implemented for electro-osmotic pumping and flow-through fluorescence sensing for the first time. Performance characteristics of macroporous silicon (> 500 µm) implemented for electro-osmotic pumping include, a very large flow effciency of 19.8 µLmin-1V-1 cm-2 and maximum pressure effciency of 86.6 Pa/V in comparison to mesoporous silica membranes with 2.8 µLmin-1V-1cm-2 flow effciency and a 92 Pa/V pressure effciency. The electrical current (I) of the EOP system for 60 V applied voltage utilizing macroporous silicon membranes is 1.02 x 10-6A with a power consumption of 61.74 x 10-6 watts. Optical measurements on mesoporous silica are performed spectroscopically from 300 nm to 1000 nm using ellipsometry, which includes, angularly resolved transmission and angularly resolved reflection measurements that extend into the infrared regime. Refractive index (n) values for oxidized and un-oxidized mesoporous silicon sample at 1000 nm are found to be 1.36 and 1.66. Fluorescence results and characterization confirm the successful pH measurement from ratiometric techniques. The sensitivity measured for fluorescein in buffer solution is 0.51 a.u./pH compared to sensitivity of ~ 0.2 a.u./pH in the case of fluorescein in porous silica template. Porous silica membranes are efficient templates for immobilization of optical dyes and represent a promising method to increase sensitivity for small variations in chemical properties. The FTSD represents a device topology suitable for application to long term monitoring of lakes and reservoirs. Unique and important contributions from this work include fabrication of a through-wafer mesoporous silica membrane that has been thoroughly characterized optically using ellipsometry. Mesoporous silica membranes are tested as a porous media in an electro-osmotic pump for generating high pressure capacities due to the nanometer pore sizes of the porous media. Further, dye immobilized mesoporous silica membranes along with macroporous silicon substrates are implemented for continuous pH measurements using fluorescence changes in a flow-through sensing device configuration. This novel integration and demonstration is completely based on silicon and implemented for the first time and can lead to miniaturized flow-through sensing systems based on MEMS technologies.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.