Modeling and Validation of Rate-Integrating Gyroscopes With $0.01~^{\circ}/\sqrt{\text{h}}$ ARW and $100~\mu^{\circ}/\sqrt{\text{Hz}}$ AWN

This work presents a predictive model for the estimation of noise on the state variables of rate-integrating gyroscopes. Linearization of the state equations allows the design of an optimal control strategy and to analytically calculate the effect of noise on the control system. After cross-checking the model against behavioral noise simulations, the control system is implemented on FPGA and the model is validated on MEMS devices fabricated in a commercial process, employing an open-loop compensation of the stiffness asymmetry to enable a part-to-part repeatability of the scale-factor within 0.15%. Experimental results show a scale-factor linearity of 0.007%, a temperature stability of ±59.5 ppm, and a bias instability (BI) less than 0.2 °/h. An unprecedented angle white noise (AWN) floor of 100 μ °/√ Hz is measured, matching the predictions of the proposed model. [2025-0207]