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MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis

An accelerometer and structural analysis technology, applied in the direction of speed/acceleration/shock measurement, testing/calibration of speed/acceleration/shock measurement equipment, neural learning methods, etc., can solve the problem of inaccurate modeling of estimated temperature drift errors, incomplete Explore problems such as temperature-related quantities of MEMS accelerometer temperature drift errors, MEMS accelerometer temperature drift error estimation inaccuracies, etc.

Active Publication Date: 2021-09-14
HARBIN ENG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional method does not fully explore the temperature-related quantity of the temperature drift error of the MEMS accelerometer, which leads to the inaccurate modeling of the estimated temperature drift error, and then makes the estimation of the MEMS accelerometer temperature drift error inaccurate. A MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis is proposed

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  • MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis
  • MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis
  • MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis

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specific Embodiment approach 1

[0028] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT One, a MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis described in the present embodiment, the method specifically includes the following steps:

[0029] Step 1. Obtain the temperature-related quantity used to estimate the temperature drift error of the MEMS accelerometer

[0030] Since the sensing circuit of the MEMS accelerometer has a comb structure, the sensing circuit of the MEMS accelerometer can be abstracted as a plate capacitor composed of a moving plate and a fixed plate. Due to the temperature dependence of the silicon-based material, when the ambient temperature When changing, the comb tooth structure undergoes structural deformation, and causes the internal structure of the MEMS accelerometer to present a three-dimensional space change, based on the plate capacitance calculation formula Deduce the capacitance output deviation before and after the ambient...

specific Embodiment approach 2

[0036] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is that the specific process of the step one is:

[0037] When the ambient temperature is T 0 , the overlapping length of the moving plate and the fixed plate is b 0 , the thickness of the comb teeth of the moving plate and the comb teeth of the fixed plate are both j 0 , the distance between the comb teeth of the moving plate and the comb teeth of the fixed plate is u0 , after simplification, the output capacitance value ΔC at this time 0 for:

[0038]

[0039] Among them, ε 0 is the dielectric constant;

[0040] When the ambient temperature change is T, it is assumed that the acceleration of the carrier to be detected and the ambient temperature are T 0 When the acceleration of the carrier to be detected is the same, the change of the ambient temperature is ΔT=T-T 0 , due to the temperature dependence of silicon-based materials, the internal ...

specific Embodiment approach 3

[0055] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the BP neural network uses the crtbp function to complete the initialization of weights and bias parameters, and uses the GA (genetic) algorithm to initialize the weights and biases. Set parameters for selection, crossover and mutation, and then use the bs2rv function to decode the data after selection, crossover and mutation, and use the decoded data as the initial particle parameters of the PSO (particle swarm optimization) algorithm;

[0056] After decoding the output of the PSO algorithm, the decoding result is used as the initial weight and bias of the BP neural network.

[0057] The fitness fitness function is used to obtain the mean square error based on the measured data, initial weight and bias as the particle fitness, and guide the PSO algorithm to update the speed and position.

[0058] Take advantage of the velocity update operator v k+1 =v k +c 1 ...

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Abstract

The invention discloses an MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis, and belongs to the field of novel micro inertial devices. According to the method, the problem that the temperature correlation quantity of the MEMS accelerometer temperature drift error is not completely explored by a traditional method, so that the temperature drift error estimation of the MEMS accelerometer is not accurate due to non-precise modeling of the estimated temperature drift error is solved. According to the method, the temperature dependence of the silicon-based material is comprehensively analyzed in detail from the perspective of the microstructure effect, the temperature dependence of the silicon-based material is well decoupled, and under the condition that the environment temperature is complex and changeable, the environmental adaptability of the MEMS accelerometer can be completely improved by compensating the temperature drift error, and the MEMS accelerometer accurately, stably and reliably outputs carrier acceleration information in real time. The method can be applied to carrier acceleration detection.

Description

technical field [0001] The invention belongs to the field of novel micro-inertial devices, and in particular relates to a MEMS accelerometer temperature drift error estimation method based on silicon microstructure analysis. Background technique [0002] With the development and progress of science and technology, human desire to obtain renewable resources is becoming increasingly strong. The deep space environment is rich in minerals and energy, and there may even be a habitable environment that can support life on Earth. In order to effectively alleviate the increasingly urgent situation of per capita natural resources on the earth, the pace of human resource exploration is gradually shifting from the surface to deep space, and a lot of manpower and material resources have been invested to solve the huge survival crisis that the earth is currently facing, such as lunar soil mining, Mars exploration, etc. However, due to the serious threat of the extremely harsh deep spac...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/25G06N3/00G06N3/04G06N3/08G01P21/00G06F119/08G06F119/14
CPCG06F30/27G06F30/25G06N3/084G06N3/006G01P21/00G06F2119/08G06F2119/14G06N3/045
Inventor 齐兵石帅帅徐陆通房磊陈嘉宇田帅帅
Owner HARBIN ENG UNIV
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