Modeling and optimization of a MEMS membrane-based acoustic-wave biosensor

Rapid, reliable, and inexpensive detection of biological and chemical species is highly advantageous in numerous situations. The ability to simultaneously detect multiple targets, for example in medical or environmental testing settings, in areas where modern laboratory equipment is not widely available, is especially desirable. The combination of acoustic wave sensing and MicroElectroMechanical Systems (MEMS) technology leads to a sensor with these capabilities. In this thesis we describe the modeling and optimization of such a membrane-based acoustic wave MEMS biosensor. Starting from an analytical model of the vibration behavior of an unloaded membrane, we model the vibration behavior of a mass-loaded membrane both computationally (using Finite Element Methods) and by using matrix perturbation analysis to develop a computationally efficient approximate analytical solution. Comparing the two methods, we find that our two models show excellent agreement for the range of mass loadings we expect to see. We then note that we can alter sensor performance by controlling the placement of chemically or biologically functionalized regions on the membrane. Our approximate analytical model lets us efficiently predict the effects of functionalization geometries, and so we can optimize performance according to a number of metrics. We develop several optimization objectives to take advantage of our ability to control sensitivity and to multiplex. We develop precise formulations for the objective functions and for constraints, both physical and design-related. We then solve our optimization problems using two complementary methods. The first is an analytical approach we developed, which is feasible for simpler problems, while the second is a stochastic optimization routine using genetic algorithms for more complex problems. Using this method we were able to confirm the solutions given by our analytical approach, and find solutions for more complicated optimization problems. Our solutions allow us to examine the tradeoffs involved in deciding where to place regions of added mass, including tradeoffs between patches and between modes. This helps to elucidate the dynamics of our system, and raises questions for further research. Finally we discuss future research directions, including further optimization possibilities for single sensors as well as for systems of multiple sensors.