Micromachined high-g accelerometers: a review

Zhao

et al. 2016

Sensors

The field of piezoresistive sensors has been undergoing a significant revolution in terms of design methodology, material technology and micromachining process. However, the temperature dependence of sensor characteristics remains a hurdle to cross. This review focuses on the issues in thermal-performance instability of piezoresistive sensors. Based on the operation fundamental, inducements to the instability are investigated in detail and correspondingly available ameliorative methods are presented. Pros and cons of each improvement approach are also summarized. Though several schemes have been proposed and put into reality with favorable achievements, the schemes featuring simple implementation and excellent compatibility with existing techniques are still emergently demanded to construct a piezoresistive sensor with excellent comprehensive performance.

Section: Introductionmentioning

confidence: 99%

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Liu

Zhao

et al. 2016

Sensors

“…However, the mechanical designs and the associated interface circuits of micromachined accelerometers have been evolving rapidly in recent years. In 2015, a review of high-g micromachined accelerometers was presented in [8]. However, low-g micromachined accelerometers (accelerometers with a sub-µg resolution are not discussed in [8].…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, a review of high-g micromachined accelerometers was presented in [8]. However, low-g micromachined accelerometers (accelerometers with a sub-µg resolution are not discussed in [8].…”

Section: Introductionmentioning

confidence: 99%

Micromachined Accelerometers with Sub-µg/√Hz Noise Floor: A Review

Chen

et al. 2020

Sensors

This paper reviews the research and development of micromachined accelerometers with a noise floor lower than 1 µg/√Hz. Firstly, the basic working principle of micromachined accelerometers is introduced. Then, different methods of reducing the noise floor of micromachined accelerometers are analyzed. Different types of micromachined accelerometers with a noise floor below 1 µg/√Hz are discussed. Such sensors can mainly be categorized into: (i) micromachined accelerometers with a low spring constant; (ii) with a large proof mass; (iii) with a high quality factor; (iv) with a low noise interface circuit; (v) with sensing schemes leading to a high scale factor. Finally, the characteristics of various micromachined accelerometers and their trends are discussed and investigated.

“…Many conditions, such as the release of space equipment [2], drop of electronic devices [3], and crashing of vehicles [4], are subjected to high-g shock excitations. In order to estimate and improve the reliability of products under shocks, high-g accelerometers are developed and widely used to measure shock responses [5]. However, accurately measuring a high-g shock signal is challenging because of the high peak value, short duration, and complicated frequency spectrum [6].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

Wen

Yao

et al. 2020

IEEE Sensors J.

A deep learning based approach is proposed to accurately recover shock signals measured from a damaged high-g accelerometer without modifying the hardware. We first conducted shock tests and collected a large dataset of shock signals with different levels of acceleration by using an efficient experimental apparatus. The training data is composed of a pair of signals simultaneously obtained from a faulty accelerometer and a high-end accelerometer (served as the ground truth). A customized autoencoder neural network is designed and trained on this dataset, aiming to map the faulty signals to their reference counterparts. Experimental results show that, with the help of deep learning, shock signals can be accurately recovered from the faulty measurements. Compared with conventional approaches that require diagnosing and replacing faulty parts, the proposed data-driven method demonstrates a highly promising solution that allows recovering corrupted signals without introducing extra work to upgrade the hardware at almost zero cost. The dataset and code of this work are made publicly available on GitHub at https://github.com/hope-yao/Sensor_Calibration.