Sub-0.1/h Bias Instability Achieved in Mode-Matched MEMS Gyroscopes Through Self-Clocking

Bias drift remains a major challenge limiting the application of MEMS vibratory gyroscopes in high-precision fields. This paper presents a bias compensation approach through self-clocking. First, this study systematically analyzes the factors contributing to bias drift under dual-force feedback control, including quadrature stiffness coupling, in-phase damping coupling, and capacitive misalignment. In addition, the impact of phase error in the drive and sense modes on the bias output is thoroughly analyzed. And correlation analysis identifies quadrature stiffness coupling and in-phase damping couplings as the dominant error sources. To address these, the proposed method first suppresses quadrature errors through phase error correction and self-clocking operation. A detailed phase error correction method in the drive and sense modes is presented. The mechanism of self-clocking in digital systems of MEMS gyroscopes is investigated, aiming to achieve stable phase-frequency characteristics by synchronizing the sampling rate with the input signal frequency. This operation significantly reduces the quadrature error coupling on the bias output. Residual in-phase damping errors are then further suppressed using a drive-mode frequency self-compensation strategy. Experimental results demonstrate that the proposed approach reduces gyroscope bias instability from 0.22∘/h to 0.06∘/h. Additionally, nonlinearity is improved significantly, with the deviation reduced from 40 ppm to 9 ppm, confirming enhanced output stability and linearity.

Authors

Institutions

Publication Details

Journal
IEEE Journal of the Electron Devices Society
Published
2025-01-01
DOI
https://doi.org/10.1109/jeds.2025.3588165
Primary Topic
Advanced MEMS and NEMS Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sub-0.1/h Bias Instability Achieved in Mode-Matched MEMS Gyroscopes Through Self-Clocking

Xuewen Liu, Guangyang Chen, Xukai Ding, Hongsheng Li et al.
IEEE Journal of the Electron Devices Society
Advanced MEMS and NEMS Technologies
article

Sub-0.1/h Bias Instability Achieved in Mode-Matched MEMS Gyroscopes Through Self-Clocking

Xuewen Liu, Guangyang Chen, Xukai Ding, Hongsheng Li, Junjie Bi
article en

Abstract

Bias drift remains a major challenge limiting the application of MEMS vibratory gyroscopes in high-precision fields. This paper presents a bias compensation approach through self-clocking. First, this study systematically analyzes the factors contributing to bias drift under dual-force feedback control, including quadrature stiffness coupling, in-phase damping coupling, and capacitive misalignment. In addition, the impact of phase error in the drive and sense modes on the bias output is thoroughly analyzed. And correlation analysis identifies quadrature stiffness coupling and in-phase damping couplings as the dominant error sources. To address these, the proposed method first suppresses quadrature errors through phase error correction and self-clocking operation. A detailed phase error correction method in the drive and sense modes is presented. The mechanism of self-clocking in digital systems of MEMS gyroscopes is investigated, aiming to achieve stable phase-frequency characteristics by synchronizing the sampling rate with the input signal frequency. This operation significantly reduces the quadrature error coupling on the bias output. Residual in-phase damping errors are then further suppressed using a drive-mode frequency self-compensation strategy. Experimental results demonstrate that the proposed approach reduces gyroscope bias instability from 0.22∘/h to 0.06∘/h. Additionally, nonlinearity is improved significantly, with the deviation reduced from 40 ppm to 9 ppm, confirming enhanced output stability and linearity.

IEEE Journal of the Electron Devices Society
Southeast University (CN)
Climate action
Openalex Percentile: Top 85%
Advanced MEMS and NEMS Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.