On the Use of Acoustic Methods for the Detection of Electrostatic Capture of Diaphragm in Capacitive MEMS Microphones
Source abstract
Abstract
Most mobile phones today have capacitive micro-electro-mechanical systems (MEMSs) microphones that use either single or dual diaphragms. Methods to detect failures easily and non-invasively have become of critical importance for microphones mobile phone manufacturers as a basis for built-in self-test (BIST) and self-repair (BISR) strategies. In that regard, a four-layer framework is presented that includes lumped element modeling (LEM), failure mode simulation, failure mode discrimination, and recovery. The frequency response of the microphone is taken as the main output to analyze. To experimentally validate this framework, this article provides a failure mode induction method based on bias voltage sweeping and four new techniques, based solely on acoustic measurements to discriminate the states of electrostatic capture for single diaphragm capacitive MEMS microphones. These include 1) analysis of an acoustic signature that is unique to electrostatic capture based on cosine similarity analysis; 2) −3 dB point measurement; 3) +3 dB point measurement; and 4) cluster analysis. Measurement of pull-in voltage and snapback voltage ranges is further demonstrated based on sensitivity measurements in laboratory conditions and response magnitude and noise power measurements in non-laboratory conditions. Up to 100% success rate in detecting electrostatic capture of diaphragm is reported for this type of device.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced MEMS and NEMS Technologies · Acoustic Wave Resonator Technologies · Mechanical and Optical Resonators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering and Physical Sciences, Institute of Sensors, Signals and Systems, Heriot-Watt University, Edinburgh Campus, Edinburgh, U.K.
Funding
Engineering and Physical Sciences Research Council (EPSRC)–Center for Doctoral Training in Embedded Intelligence
References (24)
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