Microelectromechanical Systems Microphone Research Articles

Highly-sensitive wafer-scale transfer-free graphene MEMS condenser microphones

Since the performance of micro-electro-mechanical system (MEMS)-based microphones is approaching fundamental physical, design, and material limits, it has become challenging to improve them. Several works have demonstrated graphene’s suitability as a microphone diaphragm. The potential for achieving smaller, more sensitive, and scalable on-chip MEMS microphones is yet to be determined. To address large graphene sizes, graphene-polymer heterostructures have been proposed, but they compromise performance due to added polymer mass and stiffness. This work demonstrates the first wafer-scale integrated MEMS condenser microphones with diameters of 2R = 220–320 μm, thickness of 7 nm multi-layer graphene, that is suspended over a back-plate with a residual gap of 5 μm. The microphones are manufactured with MEMS compatible wafer-scale technologies without any transfer steps or polymer layers that are more prone to contaminate and wrinkle the graphene. Different designs, all electrically integrated are fabricated and characterized allowing us to study the effects of the introduction of a back-plate for capacitive read-out. The devices show high mechanical compliances Cm = 0.081–1.07 μmPa−1 (10–100 × higher than the silicon reported in the state-of-the-art diaphragms) and pull-in voltages in the range of 2–9.5 V. In addition, to validate the proof of concept, we have electrically characterized the graphene microphone when subjected to sound actuation. An estimated sensitivity of S1kHz = 24.3–321 mV Pa−1 for a Vbias = 1.5 V was determined, which is 1.9–25.5 × higher than of state-of-the-art microphone devices while having a ~9 × smaller area.

Development of a non-resonant photoacoustic gas sensor for CO2 detection

Photoacoustic spectroscopy (PAS) is an attractive technique for gas detections and analysis. This study focuses on optimizing a photoacoustic gas sensor by analyzing key structural parameters. Through the establishment of a 3D model, simulations, and subsequent experimental validation, the impact of these parameters on the sensor's performance was explored. This comprehensive approach successfully determined the optimal structural parameters. The photoacoustic gas sensor consists of a micro-electro-mechanical systems (MEMS) emitter, a miniature photoacoustic-cell, a MEMS microphone and a circuit. The footprint of the sensor is approximately 12 mm × 12 mm × 6 mm (length × width × height) which is 10 % of conventional non-dispersive infrared gas sensors. The accuracy of the sensor is ±0.05 % in 0 % to 10 % CO2 concentrations. And the response time (T100) is 80 s. The limit of detection (LOD) in CO2 concentrations is 0.12 %. The power consumption of the sensor is only 44 mW.

Perancangan Sistem Deteksi Dan Pengenalan Perintah Suara Menggunakan Modul Esp 32 Dengan Metode Convolutional Neural Network (Cnn)

ABSTRACT
 The capabilities of microcontrollers are currently developing rapidly. Thanks to Moore's Law, the number of embedded transistors is growing exponentially. This has led to increasingly sophisticated microcontroller capabilities that are inversely proportional to price, so that we can now embed artificial intelligence on microcontrollers with the help of one of Google's APIs, TensorFlow Lite. In this final project research, a voice command detection and recognition system will be designed using a 32-bit microcontroller in the form of an ESP32 module with WiFi connectivity as the main processor to perform data processing and recognition in the form of voice commands. Voice input is taken using a MEMS (Micro Electro Mechanical System) microphone using the I2C interface. The data sent has a width of 8 bits with a sampling frequency of 44 kHz. The sampling data results will be used in the artificial intelligence training and testing process, together with a voice command library collection with a data size of 4 GB consisting of 20 word files in English, Indonesian, and noise or environmental background samples. Training and testing are carried out by converting the results of the voice signal sampling data into voice spectrum images for input to the CNN (Convolutional Neural Network) algorithm. The results of the experiment show that the voice command recognition process has an accuracy of 90% with a recognition time of less than 1 second.
 Keyword : esp32,Voice, CNN, MEMS, tensorflow Lite

Low frequency sensitivity enhancement of MEMS microphone using acoustic metamaterial

In this study, we develop a multi-physics-based simulation model for 2D-based MEMS microphone to predict and characterize its performance. The 2D material is comprised of MoS2 which is a novel substance that is predicted to revolutionize many areas of energy-related fields. Here, we apply the 2D material to the next-generation MEMS (Micro Electro Mechanical Systems) microphone. We present a model in which various parameters can be controlled and output performance is predicted. The effects of membrane size, shape, and number of layers, along with the electrode location, vent-hole size, shape, and number, on the acoustic performance of the microphone are investigated. In addition, we explore the potential low-frequency performance enhancement of the miniature microphone using acoustic metamaterial.

Acoustic Sensor Development for Quench Detection in HTS Conductors

A MEMS (Micro Electro-Mechanical System) acoustic sensor-array quench detection method has been proposed for quick, unobtrusive, and precisely localized detection of quench events in superconducting magnets. A cryogenic probe was designed and built to study the quench event in a gaseous coolant induced in a segment of an HTS tape and verify the effectiveness of acoustic MEMS sensors in detecting such events. Prior work characterized the performance of a commercially available Vesper MEMS microphone, and associated amplifiers, in cryogenic conditions which were utilized in this work. Critically, the application specific integrated circuit (ASIC) used to amplify the MEMS piezo-sensor output signal fails to operate at temperatures below 110 K. Thus, the ASIC was removed from the microphone package and replaced with a custom low temperature preamplifier which we have shown performs with no loss of function at temperatures as low as 10 K. The quench experiment is conducted using the probe within a two-stage Gifford-McMahon (GM) cryocooler in a gaseous helium environment, at cryogenic temperatures between 20 K to 60 K. Three temperature sensors within the sample region monitor temperature of the HTS sample tape and environment. The HTS sample, a 2 mm wide REBCO tape, is mounted within a square G10 tube. A linear array of the repackaged sensors is mounted to the tube to detect the quench event. The quench detection behavior using the sensors is compared with the readings from voltage taps on the sample. The response characteristics of the Vesper acoustic piezo-sensor with the newly identified, cryogenic-safe preamplifier in detecting quench events of REBCO tapes in gaseous helium are presented.

Recent Trends in Structures and Interfaces of MEMS Transducers for Audio Applications: A Review

In recent years, Micro-Electro-Mechanical Systems (MEMS) technology has had an impressive impact in the field of acoustic transducers, allowing the development of smart, low-cost, and compact audio systems that are employed in a wide variety of highly topical applications (consumer devices, medical equipment, automotive systems, and many more). This review, besides analyzing the main integrated sound transduction principles typically exploited, surveys the current State-of-the-Art scenario, presenting the recent performance advances and trends of MEMS microphones and speakers. In addition, the interface Integrated Circuits (ICs) needed to properly read the sensed signals or, on the other hand, to drive the actuation structures are addressed with the aim of offering a complete overview of the currently adopted solutions.

Comparative evaluation of omnidirectional and directional micro-electromechanical system microphone performance

As the need for directionality becomes a key requirement in audio applications, directional microphones have begun to enter the micro-electromechanical system (MEMS) design and market space, and their performance is approaching that of top-of-the-line omnidirectional MEMS microphones. This presentation examines and compares the performance limitations for both types of MEMS microphones and suggests more comprehensive methods of characterization that allow the qualities of directional MEMS microphones to be fully captured. Mechanical thermal noise caused by Brownian motion of air particles, measured with a laser Doppler vibrometer system, are shared and discussed for a variety of microphones and test structures. Parameters such as signal-to-noise ratio, total harmonic distortion, and frequency response are re-examined in order to capture the value of a microphone that exhibits directional behavior. Insights and suggestions are made for improvement and future work.

Miniature optical MEMS microphone with 14dBA noise floor

This paper explains the fundamental technology used to create an optical microphone transducer. In recent years, microelectromechanical system (MEMS) capacitive microphones have demonstrated improved performance. State-of-the-art capacitive MEMS microphones can achieve SNR in the order of 73 dBA (21 dBA noise floor) with overall dynamic range in the order of 101 dB. There are fundamental challenges to driving the performance of capacitive MEMS microphone technology in very small packages to new heights. Piezoelectric MEMS microphones have not demonstrated SNR performance >65 dBA. The next breakthrough in miniature microphone technology will come from optical MEMS microphone technology. 80dB SNR (14 dBA noise floor) with 132 dB dynamic range (146dB maximum sound pressure level) has been achieved in a very small package. This paper will review the fundamentals of optical acoustic transduction and describe some of the approaches to miniaturization of the technology.

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge.

Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

Stress analysis of circular membrane-type MEMS microphones with piezoelectric read-out

In this study, the impact of residual tensile stress on a membrane-type piezoelectric MEMS (Micro Electromechanical Systems) microphone is investigated both theoretically by FEM (finite element method) simulations and experimentally by stress measurements, by manufacturing devices and by recording the acoustic performance. Based on these findings, a design is proposed composing of segmented electrodes which are arranged close to the silicon frame as well as on the inner part of the membrane. The MEMS microphone features an aluminum nitride layer which allows a piezoelectric read-out, and multiple metal bottom and top electrodes distributed radially and azimuthally. FEM simulations were done over a large range of different layer thicknesses, membrane radii, and tensile stress levels, which indicate the existence of a sweet-spot when targeting a maximum output signal. The data showed, that thicker membranes perform better under tensile stress than thinner membranes. Furthermore, a relation between output signal and residual stress level was established, which showed that the FEM predictions agree well with the measurement results of piezoelectric microphones. • Thicker MEMS membranes preferred over thinner counterparts when tensely pre-stressed • Finite element analyses revealed sweet spot for maximizing piezoelectric output • Design propositions for stressed closed membranes without stress relaxing measures • Microphone measurements and finite element methods results agree well

Sound pressure level real-time monitoring of outdoor directional noise

A single microphone can’t select an efficient target signal from a mixed sound source when measuring the sound pressure level (SPL) of a target noise source in an outdoor environment. Therefore, a microphone array is used to enhance the target signal through beamforming to restrain the interference of non-target signals effectively. Owing to their advantages of small size and low price, micro-electro-mechanical system (MEMS) microphone arrays are widely used. However, MEMS microphone mismatch commonly occurs due to production tolerances as well as aging effects. To calibrate the SPL measurement difference caused by the whole mismatch microphone array when outdoor noise monitoring is performed, a real-time calibration method was introduced in this study by compensating for the SPL difference between the reference microphone and MEMS microphone array. The circular harmonics domain-multiple signal classification sound source localization algorithm was utilized to select the effective target signal component in the time–frequency domain, and the standard transfer functions of the reference microphone were utilized to correct the SPL measurement bias of the array. To verify the feasibility of the method, the consistency of the compensating SPL difference between multi-noise and single-target noise environments was experimentally investigated. The calibration method exhibited good accuracy and robustness based on the results and can also be applied to spherical microphone array noise monitoring device.

Cough classification of MEMS microphone signal using time-series and tabular machine learning algorithms

Cough is a protective mechanism of the proximal respiratory tract. The frequency and severity of cough provide useful information for the diagnosis of upper respiratory diseases and the evaluation of their treatments. Manual cough classification is subjective, labor-intensive, and time-consuming. In this study, a cough classification technique based on a microelectromechanical system microphone is proposed. For the classification process, various tabular and time-series machine learning algorithms were applied, and their results were compared. With respect to the time-series algorithms, the random interval decision tree, random interval spectral forest, and random convolution kernel transform (ROCKET) methods were used. With respect to the tabular algorithms, a convolution neural network (CNN) with 40 Mel-frequency cepstral coefficients (MFCCs) and a recurrent neural network with 40 MFCCs were used. Voluntary cough and noncough (throat clearing, expiration, speaking, and rest) signals were recorded from 10 healthy subjects. The ROCKET method showed the best accuracy (98.40%). In addition, while its training took the longest time (1628.80 s), this algorithm took a reasonably short time for prediction (0.27 s). The CNN showed the second-best accuracy (97.81%) with short training (454.13 s) and prediction (0.40 s) times. Thus, given its accuracy and prediction time, the ROCKET method is recommended for this type of classification over the CNN algorithm. To validate the application of the proposed methods, two methods were applied to a public Coswara data set. To classify one cough class and three non-cough classes, the ROCKET and CNN showed reasonable accuracies of 90.33% and 89.16%, respectively.

Low-cost measurement-grade microphone powered by MEMS elements and preamplifier housed in 3D printed enclosure

Acoustic measurement-grade microphones with flat frequency responses and adequate sensitivities are a necessary tool for many acousticians and vibroacoustic engineers. These microphones can often cost hundreds, if not thousands, of dollars. With the availability of microelectromechanical systems (MEMs) microphone elements and 3D printers, it is possible to construct drop-in replacements of these measurement grade microphones at the fraction of the cost. A MEMs system was designed with four elements in parallel to reduce uncorrelated noise. The system runs rail-to-rail on a 3.3VDC, Integrated Electronics Piezo-Electric (IEPE) powered preamplifier, producing a nominal sensitivity of 13 mV/Pa. The microphone body is 3D printed, and the final bill of material cost is less than 30 USD. The performance of the microphone was measured experimentally in a semi-anechoic chamber and verified by comparing the data to measurements of a commercial microphone: the PCB Piezotronics 378B02 ½” free-field microphone with a nominal sensitivity of 50 mV/Pa. This presentation will show that the MEMs microphone has a similar electrical noise floor, and lower acoustical noise floor below 1 kHz. The frequency response of the MEMs microphone is flat in the audio band through 10 kHz, making it a suitable replacement for acoustical measurements where its lower sensitivity can be tolerated.

Evaluation of Polymer Materials for MEMS Microphones

Polymers are attractive materials for the fabrication of micro-electro-mechanical system (MEMS) microphones because of their low Young’s moduli, generally lower processing temperatures than silicon, and simple fabrication; such microphones are sensitive and economical. However, an earlier polymer diaphragm revealed lower sensitivity than expected. In addition, the viscoelastic properties of polymer diaphragm have been considered to degrade the sensing performance due to a high-level mechanical noise. Here, we investigated the possibility of the polymer material as a MEMS microphone in terms of stiffness and damping. Stiffness was analyzed and experimentally verified. The obtained polymer diaphragm damping coefficient was compared with the coefficient of a silicon MEMS microphone to explore whether performance was degraded by material damping. All samples were fabricated using a thin film transfer method. SU-8 was mainly used but Polymethyl methacrylate (PMMA) was also selected for comparing the material damping effect. The measured residual stress of an SU-8 diaphragm was 18 MPa; the measured flexibility was similar to the state-of-art of a silicon-based MEMS microphone, and it was proven that there is room for further improvement through process optimization. The damping effect induced by viscoelasticity was very small, compared with the effect of air damping; indeed, the intrinsic damping effect of the polymer on device performance was negligible. [2021-0213]

Interfacing PDM MEMS Microphones with PFM Spiking Systems: Application for Neuromorphic Auditory Sensors

Neuromorphic computation processes sensors output in the spiking domain, which presents constraints in many cases when converting information to spikes, loosing, as example, temporal accuracy. This paper presents a spike-based system to adapt audio information from low-power pulse-density modulation (PDM) microelectromechanical systems microphones into rate coded spike frequencies. These spikes could be directly used by the neuromorphic auditory sensor (NAS) for frequency decomposition in different bands, avoiding the analog or digital conversion to spike streams. This improves the time response of the NAS, allowing its use in more time restrictive applications. This adaptation was conducted in VHDL as an interface for PDM microphones, converting their pulses into temporal distributed spikes following a pulse-frequency modulation scheme with an accurate inter-spike-interval, known as PDM to spikes interface (PSI). We introduce a new architecture of spike-based band-pass filter to reject DC components and distribute spikes in time. This was tested in two scenarios, first as a stand-alone circuit for its characterization, and then integrated with a NAS for verification. The PSI achieves a total harmonic distortion of -46.18 dB and a signal-to-noise ratio of 63.47 dB, demands less than 1% of the resources of a Spartan-6 FPGA and its power consumption is around 7 mW.

Design and implementation of an acoustic-vibration capacitive MEMS microphone

A novel acoustic-vibration capacitive microelectromechanical system microphone is designed, fabricated, and implemented in this paper. The new microphone consists of a rigid diaphragm and mass blocks sensitive to low-frequency vibratory and sound signals. This sensor takes advantage of the semiconductor technology to design the capacitance sensor structure by surface micromachining technology, and the inertial mass blocks are shaped using the bulk silicon micromachining technology. The structure of the anti-stiction-dimple array is designed and deployed at the bottom of the diaphragm and the backplate to avoid the risk of sensor failure by vibration stiction. The bottom and top of the backplate are designed with an anti-humidity hydrophobic insulation protective layer, which avoids adsorption of moisture and attachment of foreign particles. The thickness of the mass blocks can be controlled by the combination of the dry and the wet micromachining method, which is sensitive to different frequency responses. This study can design and produce wafer level silicon with high consistency. The inertial mass proposed in this research can be achieved through a 6 in. wafer process with >80% consistency when the thickness of the mass is around 300 μm. The sensing frequency can be reduced to below the 4 kHz frequency bandwidth with enhanced sensitivity in the ±0.5 dB range. Typical characteristic results show that the open-circuit sensitivity of the microphone is 12.63 mV/Pa (37.97 dBV/Pa) at 1 kHz (with 94 dB as the reference sound level). The total harmonic distortion and acoustic overload point are 0.21% and 121.2 dB sound pressure level, respectively. The electronic stethoscope is a typical application of this research, which can collect the characteristics and frequency spectrum of low-frequency cardiac vibration signals.

Development of Directional MEMS Microphone Single Module for High Directivity and SNR

A unidirectional microelectromechanical system (MEMS) microphone single module for handsfree and voice recognition systems in automobiles is presented. Because in-cabin noise and temperature variation affect the reduction in the handsfree and voice recognition system performance, noise suppression and increasing the signal-to-noise ratio (SNR) of the microphone are required. In this study, a capacitive MEMS microphone module with a high SNR and a unidirectional characteristic is achieved by designing the structure, package, and module of the MEMS microphone. To improve the SNR, the microphone is developed using a slit-edged membrane. The slit structure is designed to release the residual stress of the membrane to achieve improved sensitivity and SNR. The unidirectional characteristic of the microphone enables suppression of noise signals from undesired directions. The directional characteristic of the microphone is realized by attaching a porous SU-8 filter to delay the time to one of the two acoustic ports on the package. Tests on the proposed unidirectional MEMS microphone package and module show that an SNR of 62.4 dB and a front-back ratio of 27.1 dB are achieved.

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

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.

Road to Acquisition: Preparing a MEMS Microphone Array for Measurement of Fuselage Surface Pressure Fluctuations.

Preparing and pre-testing experimental setups for flight tests is a lengthy but necessary task. One part of this preparation is comparing newly available measurement technology with proven setups. In our case, we wanted to compare acoustic Micro-Electro-Mechanical Systems (MEMS) to large and proven surface-mounted condenser microphones. The task started with the comparison of spectra in low-speed wind tunnel environments. After successful completion, the challenge was increased to similar comparisons in a transonic wind tunnel. The final goal of performing in-flight measurements on the outside fuselage of a twin-engine turboprop aircraft was eventually achieved using a slim array of 45 MEMS microphones with additional large microphones installed on the same carrier to drawn on for comparison. Finally, the array arrangement of MEMS microphones allowed for a complex study of fuselage surface pressure fluctuations in the wavenumber domain. The study indicates that MEMS microphones are an inexpensive alternative to conventional microphones with increased potential for spatially high-resolved measurements even at challenging experimental conditions during flight tests.

Readout Circuits for Capacitive Sensors.

The development of microelectromechanical system (MEMS) processes enables the integration of capacitive sensors into silicon integrated circuits. These sensors have been gaining considerable attention as a solution for mobile and internet of things (IoT) devices because of their low power consumption. In this study, we introduce the operating principle of representative capacitive sensors and discuss the major technical challenges, solutions, and future tasks for a capacitive readout system. The signal-to-noise ratio (SNR) is the most important performance parameter for a sensor system that measures changes in physical quantities; in addition, power consumption is another important factor because of the characteristics of mobile and IoT devices. Signal power degradation and noise, which degrade the SNR in the sensor readout system, are analyzed; circuit design approaches for degradation prevention are discussed. Further, we discuss the previous efforts and existing studies that focus on low power consumption. We present detailed circuit techniques and illustrate their effectiveness in suppressing signal power degradation and achieving lower noise levels via application to a design example of an actual MEMS microphone readout system.