Novel Adaptive FPGA-based Self-Calibration and Self-Testing Scheme with PN Sequences for MEMS-based Inertial Sensors

We propose a novel adaptive technique based on pseudo-random (PN) sequences for self-calibration and self-testing of capacitive-based sensing and resonator microstructures, using an FPGA-implemented algorithm. The movable mass is actuated electrically with maximum length pseudo-random sequences (PN) of small amplitude, to keep the device in the linear operating regime. The frequency of the stimulus is chosen within the spectral operating range of the microdevice, such that the induced mechanical response is used for the identification of the mechanical transfer function. The proposed technique uses the steady-state and dynamic responses and it is applied to a MEMS gyroscope, for closed-loop characterization and real-time calibration. The core of the adaptive method is the implementation in FPGA of a reference model of the device under test (DUT). Experimental results demonstrate the real-time model-based identification of the damping and stiffness coefficients for the sensing mode of the fabricated vibratory microgyroscope, using modest hardware resources.