Capacitive Accelerometer Research Articles

Appropriate Selection of Frequency Range and Damping Ratio to Reduce the Resonance Effect, Lower Power Consumption, and Improve the Accuracy of the Capacitive Accelerometer

Introduction: In this work, we explored the capacitive accelerometer in depth by highlighting its advantages over other accelerometers. However, a major challenge associated with using capacitive accelerometers lies in choosing the appropriate frequency range. Method: Incorrect selection of the frequency range can cause several problems. It can decrease the accuracy of the accelerometer, increase the measurement error, and increase power consumption. To overcome these challenges, we undertook detailed modeling of the physical behaviour of the capacitive accelerometer. This modeling takes into account the mechanical and electrical properties of the sensor, including its mass, its rigidity, and the characteristics of its capacitive circuit. By simulating the developed model, we analyzed how the accelerometer reacts to different frequencies of vibratory movements. Result: This analysis led to the extraction of an important mathematical relationship that links the natural frequency of the accelerometer to the frequency of the vibratory movements to which it is subjected. Using this mathematical relationship, we determined the optimal frequency range for capacitive accelerometer operation. This precise determination of the frequency range makes it possible to significantly reduce the measurement error by avoiding resonance regimes and ensuring that the sensor operates in its maximum precision zone. Conclusion: Additionally, this approach helps improve overall measurement accuracy and minimize sensor power consumption by avoiding inefficient operating conditions.

Assessment of a system for gait parameter extraction and individual feature classification using artificial neural networks and a low-cost accelerometer

Abstract A system designed for monitoring the footsteps of a person is presented, aimed at determining characteristic and statistical parameters of the individual’s gait. This non-invasive approach utilizes a low-cost commercial capacitive accelerometer to sense the vibrations caused by each step as an individual walks on the floor. The system captures signals from the accelerometer, which are then processed to obtain different signal parameters (such as step duration, cadence, stride duration, kurtosis, skewness, etc), providing information about each subject under study. The collected information is stored in a database, and artificial neural networks are employed in this report to classify types or styles of walking, as well as to identify the person’s gender, age, and body mass index. With the implementation of classifiers, physical characteristics can be grouped, potentially focusing on diagnoses or identifications based on specific data. Finally, the results obtained from tests performed on 30 volunteers are presented, verifying the accelerometer’s performance and the algorithm’s effectiveness, with accuracy percentages up to 99.2% for classification. The results show a high level of coincidence and are promising for the future improvement of the system.

Utilizing porous dielectric material to enhance the performance of capacitive MEMS accelerometers for shaft monitoring

This research investigates the optimization of the performance of a capacitive-based microelectromechanical systems (MEMS) shaft monitoring accelerometer using a porous soft dielectric material with high polarization capability and low Young’s modulus. The nonlinear governing equations of a microbeam with a proof mass coupled with the squeeze motion equation of the polymeric dielectric material excited by shaft vibrations are extracted and solved in both the spatial and temporal domains. The studied model considers the stiffness, dissipative, and inertial effects of the polymeric material and incorporates porosity as a displacement-dependent variable, which introduces an additional nonlinearity. After discretizing the coupled nonlinear differential equations using the Galerkin method, the response in the frequency domain is obtained by applying an energy balance method, where the coefficients of the Fourier expansion of the response are found by arc-length continuation through a physical gradient descent learning-based approach method. The occurrence of secondary and primary resonances in different harmonic responses is studied for different harmonic acceleration inputs. The equivalent rigidity of the design is investigated for different shaft frequency magnitudes and different proof mass geometries. The effect of the initial porosity volume fraction of the elastomeric dielectric material on the dynamic response of the accelerometer is also examined. The sensor’s sensitivity is dependent on its geometry and properties and can vary based on the structure’s material parameters. Consequently, the selection of different geometries and materials may result in either an improvement or a deterioration of the sensor’s performance.

Improvement of Capacitive Accelerometers: Integrating Modeling, Electrode Design, Signal Processing, and Material Selection

This work offers a thorough method for integrating advanced modeling, electrode design, signal processing, and material selection techniques to maximize the performance of capacitive accelerometers. Key performance measures, including noise reduction, sensitivity, and range, can be systematically analyzed and simulated to forecast how design decisions would affect the overall behavior of the sensor. Advanced electrode designs greatly increase capacitance, which results in more precise and trustworthy sensor readings. These designs include optimizing the shape and using high-permittivity materials. Furthermore, to guarantee that the accelerometer’s output precisely represents actual motion even in noisy surroundings, noise reduction strategies including filtering and digital signal processing methods are essential. Additionally, calibration is emphasized as a critical step in preserving measurement accuracy over time and accounting for environmental changes and sensor drift. The choice of material, with an emphasis on thermally stable and high-permittivity materials, is crucial in determining the capacitance, sensitivity, and durability of the sensor. The study offers a paradigm for the creation of capacitive accelerometers that perform better across a variety of applications by striking a balance between these variables.

A Micromachined Silicon-on-Glass Accelerometer with an Optimized Comb Finger Gap Arrangement.

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized for the same comb finger area. With the fingers' length, width, and depth at 120 μm, 4 μm, and 45 μm, respectively, the optimized finger gap ratio is 2.5. The area of the proof mass is 750 μm × 560 μm, which leads to a theoretical thermomechanical noise of 9 μg/√Hz. The accelerometer has been fabricated using a modified silicon-on-glass (SOG) process, in which a groove is pre-etched into the glass to hold the metal electrode. This SOG process greatly improves the silicon-to-glass bonding yield. The measurement results show that the resonant frequency of the accelerometer is about 2.05 kHz, the noise floor is 28 μg/√Hz, and the nonlinearity is less than 0.5%.

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

New parameters for the capacitive accelerometer to reduce its measurement error and power consumption

Capacitive accelerometers are essential components in a wide range of electronic devices, enabling crucial functionalities such as touch sensitivity and proximity detection. Ensuring optimal accuracy is crucial for their effective performance in various applications. A key factor in this accuracy is the frequency margin, a parameter that significantly influences the sensor's ability to detect and respond to changes in capacitance.In this article, we will delve deeply into strategies aimed at optimizing capacitive sensors with a focus on improving their frequency margin. By exploring the methodologies and techniques that enhance the sensor's ability to operate within an ideal frequency range, we aim to improve the measurement accuracy of capacitive accelerometers by reducing measurement errors and power consumption. This optimization process involves meticulous calibration of sensor parameters such as sensitivity, resonance frequency, and damping factors to maximize performance under various environmental conditions. The new capacitive accelerometer structure improves sensitivity, linearity, and accuracy through advanced measurement setups and design, offering high-performance acceleration measurements suitable for various applications and reliable data collection and calibration.

Summary of MEMS Capacitive Accelerometer

Micro-Electro-Mechanical System (MEMS) accelerometer (MEMS accelerometer), as an important MEMS device, has been widely used in the field of high-precision electronic technology and consumer electronic products. At present, the technology of MEMS accelerometer has been mature, and the sensor based on capacitance principle, piezoelectric effect, piezoresistive effect and other principles has been widely used in many aspects of national life and production and sustainable development. Combined with and according to all kinds of references, this paper briefly describes the shape composition and sensing principle of the MEMS capacitive accelerometer, enumerates the design and error optimization research of each research group for the sensor, and explains it with the relevant research data.

Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.

In recent years, there has been an increasing use of digital vibration sensors that are based on capacitive MEMS accelerometers for machine vibration monitoring and diagnostics. These sensors simplify the design of monitoring and diagnostic systems, thus reducing implementation costs. However, it is important to understand how effective these digital sensors are in detecting rolling bearing faults. This article describes a method for determining the diagnostic sensitivity of diagnostic parameters provided by commercially available vibration sensors based on MEMS accelerometers. Experimental tests were conducted in laboratory conditions, during which vibrations from 11 healthy and faulty rolling bearings were measured using two commercial vibration sensors based on MEMS accelerometers and a piezoelectric accelerometer as a reference sensor. The results showed that the diagnostic sensitivity of the parameters depends on the upper-frequency band limit of the sensors, and the parameters most sensitive to the typical fatigue faults of rolling bearings are the peak and peak-to-peak amplitudes of vibration acceleration. Despite having a lower upper-frequency range compared to the piezoelectric accelerometer, the commercial vibration sensors were found to be sensitive to rolling bearing faults and can be successfully used in continuous monitoring and diagnostics systems for machines.

A Method of Precise Auto-Calibration in a Micro-Electro-Mechanical System Accelerometer.

A novel design of a MEMS (Micro-Electromechanical System) capacitive accelerometer fabricated by surface micromachining, with a structure enabling precise auto-calibration during operation, is presented. Precise auto-calibration was introduced to ensure more accurate acceleration measurements compared to standard designs. The standard mechanical structure of the accelerometer (seismic mass integrated with elastic suspension and movable plates coupled with fixed plates forming a system of differential sensing capacitors) was equipped with three movable detection electrodes coupled with three fixed electrodes, thus creating three atypical tunneling displacement transducers detecting three specific positions of seismic mass with high precision, enabling the auto-calibration of the accelerometer while it was being operated. Auto-calibration is carried out by recording the accelerometer indication while the seismic mass occupies a specific position, which corresponds to a known value of acting acceleration determined in a pre-calibration process. The diagram and the design of the mechanical structure of the accelerometer, the block diagram of the electronic circuits, and the mathematical relationships used for auto-calibration are presented. The results of the simulation studies related to mechanical and electric phenomena are discussed.

Dependence of Structural Design on Effective Young's Modulus of Ti/Au Multi-layered Micro-cantilevers

Gold-based micro-electro-mechanical-systems (Au-MEMS) capacitive accelerometers can simultaneously realize high sensitivity and miniaturization because of the high mass density of Au. In order to further improve the sensitivity of the Au-MEMS capacitive accelerometers, Young's modulus of the cantilever-like spring part connected to the movable component is a key parameter. Considering the size effect in the mechanical property of metallic materials on micro-scale, the design of the spring part is expected to reflect their Young's modulus; that is, effective Young's modulus (Eeff). In this study, we clarify effects of the structural designs of the Au-based micro-cantilevers on their Eeff by experiments and finite element analyses (FEA) simulations. The Eeff of the Au micro-cantilevers having Ti/Au multi-layered structures is evaluated by resonance frequency method, which demonstrates the key structural parameters affecting their Eeff. The FEA calculations show a consistent trend with that observed in the experimental results.

A high aspect ratio surface micromachined accelerometer based on a SiC-CNT composite material

Silicon carbide (SiC) is recognized as an excellent material for microelectromechanical systems (MEMS), especially those operating in challenging environments, such as high temperature, high radiation, and corrosive environments. However, SiC bulk micromachining is still a challenge, which hinders the development of complex SiC MEMS. To address this problem, we present the use of a carbon nanotube (CNT) array coated with amorphous SiC (a-SiC) as an alternative composite material to enable high aspect ratio (HAR) surface micromachining. By using a prepatterned catalyst layer, a HAR CNT array can be grown as a structural template and then densified by uniformly filling the CNT bundle with LPCVD a-SiC. The electrical properties of the resulting SiC-CNT composite were characterized, and the results indicated that the electrical resistivity was dominated by the CNTs. To demonstrate the use of this composite in MEMS applications, a capacitive accelerometer was designed, fabricated, and measured. The fabrication results showed that the composite is fully compatible with the manufacturing of surface micromachining devices. The Young’s modulus of the composite was extracted from the measured spring constant, and the results show a great improvement in the mechanical properties of the CNTs after coating with a-SiC. The accelerometer was electrically characterized, and its functionality was confirmed using a mechanical shaker.

Serpentine spring design technique for high sensitivity MEMS capacitive accelerometer fabricated by gold multi-layer metal technology

This paper describes a design technique for serpentine spring with the edge span beam L/2. In order to improve design accuracy, we investigate the key structure parameters influenced by the fabrication process for MEMS device, comparing finite element analysis (FEA) and analytical models. It is found that the Fedder model as the analytical model is suitable for the spring constant characteristics of FEA with the edge span beam L/2. Further, the thickness and width are found to be the key structure parameters and the dominant factor of variation regarding the serpentine spring. Based on the analysis, we propose a spring design technique. The experimental results show that the variation between the measured data of 4.46 N m−1 and the design value of 4.15 N m−1 was 7.5%, satisfying the target of ±10%. It is confirmed that the proposed technique can establish the serpentine spring for high sensitivity gold proof-mass MEMS accelerometers.

Design and performance analysis of a MEMS Based Area-Variation Capacitive Accelerometer with Readout Circuit

The structural optimization of the device is used in the present work to demonstrate the improvement of the device sensitivity for a MEMS accelerometer based on capacitive principle due to overlap area change between electrodes. The proof mass of the device is made up of a few parallel fingers those are joined together. Proof mass is supported by flexible mechanical beams that resemble springs is suspended over fixed electrodes and are fastened to the substrate. The greatest displacement that the proof mass can suffer with application of acceleration is determined for the specific construction. The connected beams' width and length were changed, and ANSYS FEA software was used to model the reaction. Sensitivity of the device is analyzed and discussed based on the findings of various device geometry measurements, and suggestions for improving sensitivity are also made. Additionally, a signal conditioning circuit that changes the capacitance to voltage as a result of the proof mass deflecting differently is described. These discoveries might help designers to create capacitive MEMS accelerometers with increased sensitivity.

Clarification of Geometric Effects on Long-term Structural Stability of Ti/Au Multi-layered Micro-cantilevers

A gold micro-electro-mechanical-systems (Au-MEMS) capacitive accelerometer having Ti/Au multi-layered structures is a promising device to detect very weak accelerations, such as muscle sounds, because of the high mass density of Au. However, Au is a soft metal, which raises concerns about the structural stability of the Au-MEMS capacitive accelerometers for practical use. In this work, we clarify the key geometric parameters to enhance their long-term structural stability by conducting a long-term vibration test for a total of 240 Ti/Au multi-layered micro-cantilevers with different geometric parameters, such as the length, width, and thickness of the micro-cantilevers, and the number of Ti/Au multi-layered structures. The long-term structural stability is evaluated from the change in the tip height of the micro-cantilevers before and after the vibration tests. These tests demonstrate that the micro-cantilevers with a shorter length, larger thickness, and more Ti/Au multi-layered structures are found to show better long-term structural stability.

Warpage Study of Electrodeposited-Au Micro-Components with Ti/Au Multi-Layered Structures toward MEMS Applications

Electrodeposited Au-based micro-electromechanical systems (MEMS) capacitive accelerometers with Ti/Au multi-layered structures are promising devices for detecting very low accelerations due to the high mass density of Au. On the other hand, the large difference in the coefficients of thermal expansion (CTE) between Ti and Au causes warpage in the electrodeposited-Au proof masses in MEMS capacitive accelerometers, which reduces the accuracy of the acceleration sensing. In this study, the warpage behavior of electrodeposited Au proof masses with Ti/Au structures is evaluated to clarify factors affecting their structural stability. Ti/Au multi-layered proof masses with four different multi-layered structures and seven different sizes are fabricated. The warpage of the proof mass is quantified by the height information determined by a 3D optical microscope. The 3D optical microscopy analyses show that the observed concave warpage is suppressed by increasing the layer number of Ti/Au layered structures in the proof mass. The warpage is also investigated by Finite Element Analysis (FEA) simulations. The experimental results are in good agreement with the FEA simulations.

MEMS capacitive accelerometer: A review

Micro-electro-mechanical systems sensors are integrated systems used in many fields such as consumer electronics, the automobile industry, and biomedical, and their dimensions change between micrometers and millimeters. MEMS capacitive accelerometers are the most widely used sensor type among MEMS accelerometer sensors. As a result of the external force applied to the capacitive accelerometer sensor, the proof mass inside the sensor moves, and the capacitive change is measured as an electrical signal using reading circuits. In this review paper, general information about MEMS sensors is given, and a comprehensive review is made of MEMS capacitive accelerometers. In the study, the dynamic circuit of the MEMS capacitive accelerometer is given, and the calculation of the important values for the mechanical and electronic structure during the design of the capacitive MEMS accelerometer is explained. In addition, information about the readout circuits used to convert the capacitive change to voltage is given. Finally, the fabrication processes used to produce the final product are explained, and the studies on sample fabrication processes found in the literature are mentioned.

Analysis of the Frequency-Dependent Vibration Rectification Error in Area-Variation-Based Capacitive MEMS Accelerometers.

The presence of strong ambient vibrations could have a negative impact on applications such as high precision inertial navigation and tilt measurement due to the vibration rectification error (VRE) of the accelerometer. In this paper, we investigate the origins of the VRE using a self-developed MEMS accelerometer equipped with an area-variation-based capacitive displacement transducer. Our findings indicate that the second-order nonlinearity coefficient is dependent on the frequency but the VRE remains constant when the displacement amplitude of the excitation is maintained at a constant level. This frequency dependence of nonlinearity is a result of several factors coupling with each other during signal conversion from acceleration to electrical output signal. These factors include the amplification of the proof mass's amplitude as the excitation frequency approaches resonance, the nonlinearity in capacitance-displacement conversion at larger displacements caused by the fringing effect, and the offset of the mechanical suspension's equilibrium point from the null position of the differential capacitance electrodes. Through displacement transducer and damping optimization, the second-order nonlinearity coefficient is greatly reduced from mg/g2 to μg/g2.

Fatigue Analysis and Estimation of the Number of Exposure Cycles until the Failure of the Sensitive Element of a Micromechanical Capacitive Accelerometer

Fatigue Analysis and Estimation of the Number of Exposure Cycles until the Failure of the Sensitive Element of a Micromechanical Capacitive Accelerometer

Impact of Thermal Variations and Soldering Process on Performance and Behavior of MEMS Capacitive Accelerometers

Impact of Thermal Variations and Soldering Process on Performance and Behavior of MEMS Capacitive Accelerometers