The research objective of this award is to develop a multi-scale fractal topography model for contact analysis in RF-MEMS (Radio Frequency MEMS) to improve the reliability in its design and manufacturing. Topography of mating surfaces and contact mechanics determines the performance quality of RF-MEMS. Topography is a random field and has scale-dependent regular and self-affine fractal signal features over several orders of magnitude, ranging from 1nm to 100ìm. In this award, a novel and widely-applicable methodology for characterizing and modeling multi-scale fractal topography will be developed that will then be incorporated into contact analysis for RF-MEMS performance and reliability improvement. The four key research objectives are: the development of a rigorous statistical methodology for micro/nano topography acquisition, characterization, and modeling in the face of multi-scale, fractal, and random surface structures; 2) development of a statistical regular-fractal topography model that has a high fidelity and correlation to real topography; 3) creation of a micro/nano-contact simulation that takes into account elasto-plastic deformation, multi-scale hierarchical asperity, and statistical topography; and 4) generation of new knowledge on failure mechanisms obtained from the correlation among topography, contact simulation, and experimental study.

Successful completion of this research would lead to a new model for surface topography and its associated contact mechanics for use in micro-electro-mechanical systems. The creation of a new computational infrastructure will be quite impactful. The results of the study can be used directly in MEMS industries for measuring, characterizing, and quantifying the distribution and evolution of charge and wear of surfaces in MEMS switches over time and for studying consistency and variability of surfaces, enhancing both design and manufacturing. The results of this award will disseminated widely via professional academic and industrial collaborative networks, journal publications, and conferences. Under-represented graduate and undergraduate students will participate in this research and will receive valuable exposure to the challenges of information extraction, statistical modeling and metrological techniques used in data acquisition. Some of the results from the research will be integrated into undergraduate and graduate courses, benefiting all the engineering students at the university. The results of this study will also be shared to incoming freshmen through the Freshmen Summer Institute (FSI) program.

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University of Massachusetts, Dartmouth
North Dartmouth
United States
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