Implementation of Novel Temperature Drift Errors Precise Estimation System for Capacitive MEMS Accelerometers

To eliminate Temperature Drift Errors (TDE) of Capacitive MEMS Accelerometers (CMA) precisely and decouple its temperature independence to expand its applications, a novel TDE precise estimation system is proposed. Firstly, TDE of CMA are traced accurately in microstructure deformation, and all-new temperature correlations are explored, like ambient temperature, ambient temperature variation and its square. Then, a novel TDE precise estimation model is formed with temperature correlations and Radial Basis Function Neural Network (RBFNN). Thirdly, the hardware circuits of novel TDE precise estimation system is designed and implemented. Last, temperature experiment is made to verify the novel TDE precise estimation system, and its novel model is compared in accuracy with the conventional model based on Back-Propagation Neural Network (BPNN) by evaluating bias stability of CMA’s outputs. The experimental results show bias stability of CMA’s outputs compensated by the novel model are reduce to 30% of the conventional model, which means that novel TDE precise estimation system estimate TDE of CMA much more precisely to decouple its temperature independence significantly.