Optimization of capacitive acoustic resonant sensor using numerical simulation and design of experiment

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 capacit...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 15; no. 4; pp. 8945 - 8967
Main Authors Haque, Rubaiyet Iftekharul, Loussert, Christophe, Sergent, Michelle, Benaben, Patrick, Boddaert, Xavier
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 16.04.2015
MDPI
Subjects
acoustic sensor
Acoustics
Bandwidths
Computer simulation
Design of experiments
Devices
Electrodes
Mathematical models
Mechanics
Membranes
numerical simulation
Optimization
Physics
resonance
response surface method
Sensors
Simulation
Transducers
France
response surface method
optimization
CONDENSER MICROPHONE
SILICON
MEMBRANES
AIR
resonance
acoustic sensor
numerical simulation
design of experiments
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s150408945