MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Optimal and Robust Design Method for Two-Chip Out-of-Plane Microaccelerometers

In this paper, an optimal and robust design method to implement a two-chip out-of-plane microaccelerometer system is presented. The two-chip microsystem consists of a MEMS chip for sensing the external acceleration and a CMOS chip for signal processing. An optimized design method to determine the device thickness, the sacrificial gap, and the vertical gap length of the M EMS sensing element is applied to minimize the fundamental noise level and also to achieve the robustness to the fabrication variations. In order to cancel out the offset and gain variations due to parasitic capacitances and process variations, a digitally trimmable architecture consisting of an 11 bit capacitor array is adopted in the analog front-end of the CMOS capacitive readout circuit. The out-of-plane microaccelerometer has the scale factor of 372 mV/g∼389 mV/g, the output nonlinearity of 0.43% FSO∼0.60% FSO, the input range of ±2 g and a bias instability of 122 μg∼229 μg. The signal-to-noise ratio and the noise equivalent resolution are measured to be 74.00 dB∼75.23 dB and 180 μg/rtHz∼190 μg/rtHz, respectively. The in-plane cross-axis sensitivities are measured to be 1.1%∼1.9% and 0.3%∼0.7% of the out-of-plane sensitivity, respectively. The results show that the optimal and robust design method for the MEMS sensing element and the highly trimmable capacity of the CMOS capacitive readout circuit are suitable to enhance the die-to-die uniformity of the packaged microsystem, without compromising the performance characteristics

Micromachined vibratory gyroscopes controlled by a high order band-pass sigma delta modulator.

Abstract—This work reports on the design of novel closed-loop control systems for the sense mode of a vibratory-rate gyroscope based on a high-order sigma-delta modulator (SDM). A low-pass and two distinctive bandpass topologies are derived, and their advantages discussed. So far, most closed-loop force-feedback control systems for these sensors were based on low-pass SDM’s. Usually, the sensing element of a vibratory gyroscope is designed with a high quality factor to increase the sensitivity and, hence, can be treated as a mechanical resonator. Furthermore, the output characteristic of vibratory rate gyroscopes is narrowband amplitude- modulated signal. Therefore, a bandpass M is a more appropriate control strategy for a vibratory gyroscope than a low-pass SDM. Using a high-order bandpass SDM, the control system can adopt a much lower sampling frequency compared with a low-pass SDM while achieving a similar noise floor for a given oversampling ratio (OSR). In addition, a control system based on a high-order bandpass SDM is superior as it not only greatly shapes the quantization noise, but also alleviates tonal behavior, as is often seen in low-order SDM control systems, and has good immunities to fabrication tolerances and parameter mismatch. These properties are investigated in this study at system level

A three-axis accelerometer for measuring heart wall motion

This thesis presents the work carried out in the design, simulation, fabrication and testing of miniaturised three-axis accelerometers. The work was carried out at the Faculty of Science and Engineering at Vestfold University College (Tønsberg, Norway), the MIcroSystems Engineering Centre (MISEC) at Heriot-Watt University and in collaboration with the Interventional Centre at Rikshospitalet University Hospital (Oslo, Norway). The accelerometers presented in this thesis were produced to be stitched to the surface of human hearts. In doing so they are used to measure the heart wall motion of patients that have just undergone heart bypass surgery. Results from studies carried out are presented and prove the concept of using such sensors for the detection of problems that can lead to the failure of heart bypasses. These studies were made possible using commercially available MEMS (MicroElectroMechanical Systems) three-axis accelerometers. However, the overall size of these sensors does not meet the requirements deemed necessary by the medical team (2(W) 2(H) 5(L) mm3) and fabrication activities were necessary to produce custom-made sensors. Design verification and performance modelling were carried out using Finite Element Analysis (FEA) and these results are presented alongside relevant analytical calculations. For fabrication, accelerometer designs were submitted to three foundry processes during the course of the work. The designs utilise the piezoresistive effect for the acceleration sensing and fabrication was carried out by bulk micromachining. Results of the characterisaton of the sensors are presente

High-resolution seismocardiogram acquisition and analysis system

Several devices and measurement approaches have recently been developed to perform ballistocardiogram (BCG) and seismocardiogram (SCG) measurements. The development of a wireless acquisition system (hardware and software), incorporating a novel high-resolution micro-electro-mechanical system (MEMS) accelerometer for SCG and BCG signals acquisition and data treatment is presented in this paper. A small accelerometer, with a sensitivity of up to 0.164 µs/µg and a noise density below 6.5 µg/ Hz is presented and used in a wireless acquisition system for BCG and SCG measurement applications. The wireless acquisition system also incorporates electrocardiogram (ECG) signals acquisition, and the developed software enables the real-time acquisition and visualization of SCG and ECG signals (sensor positioned on chest). It then calculates metrics related to cardiac performance as well as the correlation of data from previously performed sessions with echocardiogram (ECHO) parameters. A preliminarily clinical study of over 22 subjects (including healthy subjects and cardiovascular patients) was performed to test the capability of the developed system. Data correlation between this measurement system and echocardiogram exams is also performed. The high resolution of the MEMS accelerometer used provides a better signal for SCG wave recognition, enabling a more consistent study of the diagnostic capability of this technique in clinical analysis.This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941

Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks