Input–Output-Improved Reservoir Computing Based on Duffing Resonator Processing Dynamic Temperature Compensation for MEMS Resonant Accelerometer

An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature ch...

Full description

Saved in:
Bibliographic Details
Published in:Micromachines (Basel) Vol. 14; no. 1; p. 161
Main Authors: Guo, Xiaowei, Yang, Wuhao, Zheng, Tianyi, Sun, Jie, Xiong, Xingyin, Wang, Zheng, Zou, Xudong
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 08.01.2023
MDPI
Subjects:
Accelerometers
Accuracy
algorithm optimization
Algorithms
dynamic temperature compensation
Edge computing
Feasibility studies
MEMS resonant accelerometer
Microelectromechanical systems
nonlinear MEMS resonator
Optimization
Polynomials
Real time
reservoir computing
Resonant frequencies
Resonators
Sensors
Silicon
Software
Temperature compensation
reservoir computing
algorithm optimization
MEMS resonant accelerometer
nonlinear MEMS resonator
dynamic temperature compensation
ISSN:2072-666X, 2072-666X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature changing due to the hysteresis of the temperature response of the accelerometer. To address this issue, we propose a novel reservoir computing (RC) structure based on a nonlinear silicon resonator, which is specifically improved for predicting dynamic information that is referred to as the input–output-improved reservoir computing (IOI-RC) algorithm. It combines the polynomial fitting with the RC on the input data mapping ensuring that the system always resides in the rich nonlinear state. Meanwhile, the output layer is also optimized by vector concatenation operation for higher memory capacity. Therefore, the new system has better performance in dynamic temperature compensation. In addition, the method is real-time, with easy hardware implementation that can be integrated with MEMS sensors. The experiment’s result showed a 93% improvement in IOI-RC compared to raw data in a temperature range of −20–60 °C. The study confirmed the feasibility of RC in realizing dynamic temperature compensation precisely, which provides a potential real-time online temperature compensation method and a sensor system with edge computing.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2072-666X
2072-666X
DOI:10.3390/mi14010161