System-level design of a thermally stable, highly robust MEMS tuning fork gyroscope with enhanced sensitivity for wearable and robotic applications
Faraz Javaid, Amir Hamza, Mohsin Islam Tiwana, Muhammad Osama Ali, Uzair Bashir, Muhammad Mubasher Saleem
- Year
- 2025
- Citations
- 1
Abstract
This paper presents the system-level design and analysis of a 39.08 kHz, split mode, thermally stable, microelectromechanical systems (MEMS) tuning fork gyroscope (TFG) with enhanced sensitivity, exhibiting high robustness along drive and sense axes against vibrations and shocks. The gyroscope is designed on a commercially available wafer-level vacuum-packaged MEMS integrated design for inertial sensors (MIDIS) process by Teledyne DALSA Semiconductor Inc. The TFG has a novel lever type drive and diamond shaped sense mechanisms to enhance robustness along both axes. Enhanced sensitivity is achieved by implementing a large drive displacement and sense mode electrical tuning mechanism along with a highly optimized decoupling mechanism. Quadrature error is reduced electronically by strategically placed error correction plates. Additionally, drive sense combs are used for scale factor correction. System-level working of TFG with integrated readout microelectronics is verified through the finite element method (FEM) and transient simulation based analysis using CoventorWare and Simulink software. The simulation results indicate vibration resistance with a maximum displacement of 14.89 μm and 1.37 μm for 1000 g and shock resistance with a maximum displacement of 14.95 μm and 1.38 μm for 25 μs, 1000 g shock signal in drive and sense directions, respectively, without structural damage. The system achieves an output bias thermal stability of 0.067 μV/°C. Moreover, the system achieves an improved capacitive and voltage sensitivity of 20.75 fF/(°/s) and 9.34 mV/(°/s), respectively. The results achieved make the proposed MEMS TFG useful in distributed sensors, micro-robots, wearable electronics, and high-end applications.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991