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Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight a...

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Autores principales: Martínez, Javier, Asiain, David, Beltrán, José Ramón
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026479/
https://www.ncbi.nlm.nih.gov/pubmed/35457889
http://dx.doi.org/10.3390/mi13040584
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author Martínez, Javier
Asiain, David
Beltrán, José Ramón
author_facet Martínez, Javier
Asiain, David
Beltrán, José Ramón
author_sort Martínez, Javier
collection PubMed
description Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six tests in orthogonal directions and a thermal chamber with a relative error difference of 0.3%.
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spelling pubmed-90264792022-04-23 Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers Martínez, Javier Asiain, David Beltrán, José Ramón Micromachines (Basel) Article Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six tests in orthogonal directions and a thermal chamber with a relative error difference of 0.3%. MDPI 2022-04-08 /pmc/articles/PMC9026479/ /pubmed/35457889 http://dx.doi.org/10.3390/mi13040584 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martínez, Javier
Asiain, David
Beltrán, José Ramón
Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title_full Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title_fullStr Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title_full_unstemmed Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title_short Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers
title_sort self-calibration technique with lightweight algorithm for thermal drift compensation in mems accelerometers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026479/
https://www.ncbi.nlm.nih.gov/pubmed/35457889
http://dx.doi.org/10.3390/mi13040584
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