Sensitivity Recalibration of MEMS Microphones to Compensate Drift and Environmental Influences

Walser, S.; Loibl, M.; Winter, Matthias; Siegel, C.; Feiertag, G.

doi:10.1016/j.proeng.2016.11.508

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Concerning the variations in the microphones’ sensitivity, it is possible to reduce such variations by modifying the sensitivity levels through programming the gain and the bias voltage [ 45 ]. Additionally, we consider that the estimated error in the sensitivity of each individual microphone can be compensated through proper calibration in dedicated environments for noise analysis, such as laboratories endowed with a reverberant acoustic chamber.…”

Section: Sampling Process Optimizationsmentioning

confidence: 99%

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

Zamora

Vera

Calafate

et al. 2018

Sensors

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Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client–server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.

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Section: Sampling Process Optimizationsmentioning

confidence: 99%

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

Zamora

Vera

Calafate

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The discrimination methods are based on acoustic fingerprint as detailed later, -3 dB point, +3 dB point measurements and a data clustering algorithm. While one of the methods is based on the same principle presented in the sensitivity work of Walser et al [7], which was conducted in laboratory settings, this technique is developed further with demonstration within an in-field setup. Our methods are suitable for deployment both in laboratory conditions during the manufacturing or product return stages, or during device operation, making them ideal candidates as BIST methods.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Acoustic Methods for the Detection of Electrostatic Capture of Diaphragm in Capacitive MEMS Microphones

Hantos

Simon

Desmulliez

2022

IEEE Trans. Compon., Packag. Manufact. Technol.

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Most mobile phones today have capacitive microelectromechanical systems (MEMS) microphones that use either single or dual diaphragm. 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 modelling (LEM), failure mode simulation, failure mode discrimination and recovery. The frequency response of the microphone is taken as the main output to analyse. 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 a) analysis of an acoustic signature that is unique to electrostatic capture based on cosine similarity analysis, b) -3 dB point measurement, C) +3 dB point measurement, and d) 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.

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Acoustic methods for detection of specific failure modes in capacitive MEMS microphones

Hantos

Desmulliez

2020

2020 IEEE 8th Electronics System-Integration Technology Conference (ESTC)

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Sensitivity Recalibration of MEMS Microphones to Compensate Drift and Environmental Influences

Cited by 4 publications

References 5 publications

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

On the Use of Acoustic Methods for the Detection of Electrostatic Capture of Diaphragm in Capacitive MEMS Microphones

Acoustic methods for detection of specific failure modes in capacitive MEMS microphones

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