In this paper, we propose a novel design and optimization environment for inertial MEMS devices based on a computationally efficient schematization of the structure at the a device level. This allows us to obtain a flexible and efficient design optimization tool, particularly useful for rapid device prototyping. The presented design environment--handles the parametric generation of the structure geometry, the simulation of its dynamic behavior, and a gradient-based layout optimization. The methodology addresses the design of general inertial MEMS devices employing suspended proof masses, in which the focus is typically on the dynamics associated with the first vibration modes. In particular, the proposed design tool is tested on a triaxial beating-heart MEMS gyroscope, an industrially relevant and adequately complex example. The sensor layout is schematized by treating the proof masses as rigid bodies, discretizing flexural springs by Timoshenko beam finite elements, and accounting for electrostatic softening effects by additional negative spring constants. The MEMS device is then optimized according to two possible formulations of the optimization problem, including typical design requirements from the MEMS industry, with particular focus on the tuning of the structural eigenfrequencies and on the maximization of the response to external angular rates. The validity of the proposed approach is then assessed through a comparison with full FEM schematizations: rapidly prototyped layouts at the device level show a good performance when simulated with more complex models and therefore require only minor adjustments to accomplish the subsequent physical-level design.
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http://dx.doi.org/10.3390/s21155064 | DOI Listing |
Sensors (Basel)
December 2024
Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.
This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors' previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
In recent years, microelectromechanical systems (MEMS) technology has developed rapidly, and low precision inertial devices have achieved small volume, light weight, and mass production. Under this background, array technology has emerged to achieve high precision inertial measurement under the premise of low cost. This paper reviews the development of MEMS inertial measurement unit (IMU) array technology.
View Article and Find Full Text PDFMicromachines (Basel)
November 2024
School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm.
View Article and Find Full Text PDFMicromachines (Basel)
November 2024
The School of Electrical and Information, Hunan Institute of Engineering, Xiangtan 411104, China.
To address problems in the integrated navigation error law of unmanned aerial vehicles (UAVs), this paper proposes a method for measuring the error rule in visual inertial odometry based on scene matching corrections. The method involves several steps to build the solution. Firstly, separate models were constructed for the visual navigation model, the Micro-Electromechanical System (MEMS) navigation model, and the scene matching correction model.
View Article and Find Full Text PDFArch Gerontol Geriatr
February 2025
Soochow University School of Physical Education and Sports, China. Electronic address:
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