MEMS microphones with narrow sensitivity distribution

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

doi:10.1016/j.sna.2016.04.051

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…The uniform sensitivity over voice frequency range enables the easy processing of speech recognition with single channel. However, the condenser‐type sensor has demerits such as insufficient sensitivity, limited recognition distance, high power consumption and the unstable circuit of large amplification . The piezoresistive sensor detects the sound by sensing the resistance change depending on the movement of membrane .…”

Section: Introductionmentioning

confidence: 99%

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

et al. 2019

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Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self‐powered sensors, machine learning algorithms, and speaker recognition.

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Section: Introductionmentioning

confidence: 99%

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

et al. 2019

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“…This paper explains the designing process of an acoustic camera with micro electro-mechanical systems (MEMS) microphones [1,2,3]. Nowadays different types of MEMS microphones are an important part of modern electronics (see Figure 1 [4]).…”

Section: Introductionmentioning

confidence: 99%

Acoustic Camera Design with Different Types of MEMS Microphone Arrays

Grubeša¹,

Stamać²,

Suhanek³

2019

AJESE

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This paper investigates different approaches in designing an acoustic camera with respect to the shape of the camera as well as the number of microphones and their position on the camera. Micro electro-mechanical systems (MEMS) microphones are used in this research for the purpose of designing an acoustic camera. Several simulations implemented in MATLAB were performed for square MEMS microphone arrays, bearing in mind our primary goal, which is to design a broadband frequency range acoustic camera with MEMS microphones. In addition, a microphone array in the shape of a hemisphere was designed in order to compare all of the obtained results. Results gathered in the simulations have shown that using the square arrays and a hemispherical array enables us to construct four different broadband frequency range acoustic cameras. All of the considered versions of an acoustic camera have a respectable gain in the desired direction (i.e. the gain of the main lobe) and, in addition, a significant attenuation of side lobes. Keeping in mind the aforementioned requirements (i.e. the main lobe gain and attenuation of side lobes) it can be concluded that, from all of the considered designs, the best design is the acoustic camera with 24 MEMS microphone square array.

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“…Most commercial MEMS acoustic sensors in IoT applications are capacitive-type devices due to their compatibility with the standard complementary metal–oxide–semiconductor (CMOS) process in terms of form factor, thermal stability, and linear frequency response. The capacitive-type MEMS acoustic sensor [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] is composed of two plates, namely, a rigid back plate fixed on a substrate and a flexible diaphragm that detects an input acoustic signal. The moving plate should respond flexibly to the input sound pressure, and the hard bottom plate must have etch holes to reduce the damping effect.…”

Section: Introductionmentioning

confidence: 99%

Wafer-Level-Based Open-Circuit Sensitivity Model from Theoretical ALEM and Empirical OSCM Parameters for a Capacitive MEMS Acoustic Sensor

Lee

Kim

et al. 2019

Sensors

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We present a simple, accurate open-circuit sensitivity model based on both analytically calculated lumped and empirically extracted lumped-parameters that enables a capacitive acoustic sensor to be efficiently characterized in the frequency domain at the wafer level. Our mixed model is mainly composed of two key strategies: the approximately linearized electric-field method (ALEM) and the open- and short-calibration method (OSCM). Analytical ALEM can separate the intrinsic capacitance from the capacitance of the acoustic sensor itself, while empirical OSCM, on the basis of one additional test sample excluding the membrane, can extract the capacitance value of the active part from the entire sensor chip. FEM simulation verified the validity of the model within an error range of 2% in the unit cell. Dynamic open-circuit sensitivity is modelled from lumped parameters based on the equivalent electrical circuit, leading to a modelled resonance frequency under a bias condition. Thus, eliminating a complex read-out integrated circuit (ROIC) integration process, this mixed model not only simplifies the characterization process, but also improves the accuracy of the sensitivity because it considers the fringing field effect between the diaphragm and each etching hole in the back plate.

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MEMS microphones with narrow sensitivity distribution

Cited by 22 publications

References 7 publications

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Acoustic Camera Design with Different Types of MEMS Microphone Arrays

Wafer-Level-Based Open-Circuit Sensitivity Model from Theoretical ALEM and Empirical OSCM Parameters for a Capacitive MEMS Acoustic Sensor

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