A capacitive acoustic resonator developed by combining three-dimensional (3D) printing and two-dimensional (2D) printed electronics technique is described. During this work, a patterned bottom structure with rigid backplate and cavity is fabricated directly by a 3D printing method, and then a direct write inkjet printing technique has been employed to print a silver conductive layer. A novel approach has been used to fabricate a diaphragm for the acoustic sensor as well, where the conductive layer is inkjet-printed on a pre-stressed thin organic film. After assembly, the resulting structure contains an electrically conductive diaphragm positioned at a distance from a fixed bottom electrode separated by a spacer. Measurements confirm that the transducer acts as capacitor. The deflection of the diaphragm in response to the incident acoustic single was observed by a laser Doppler vibrometer and the corresponding change of capacitance has been calculated, which is then compared with the numerical result. Observation confirms that the device performs as a resonator and provides adequate sensitivity and selectivity at its resonance frequency.
Optimization of the acoustic resonant sensor requires a clear understanding of how the output responses of the sensor are affected by the variation of different factors. During this work, output responses of a capacitive acoustic transducer, such as membrane displacement, quality factor, and capacitance variation, are considered to evaluate the sensor design. The six device parameters taken into consideration are membrane radius, backplate radius, cavity height, air gap, membrane tension, and membrane thickness. The effects of factors on the output responses of the transducer are investigated using an integrated methodology that combines numerical simulation and design of experiments (DOE). A series of numerical experiments are conducted to obtain output responses for different combinations of device parameters using finite element methods (FEM). Response surface method is used to identify the significant factors and to develop the empirical models for the output responses. Finally, these results are utilized to calculate the optimum device parameters using multi-criteria optimization with desirability function. Thereafter, the validating experiments are designed and deployed using the numerical simulation to crosscheck the responses.
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