Mrigank Sharma

Ultrasensitive resonant MEMS transducers with tuneable coupling

This paper introduces a novel ultrasensitive resonant MEMS transducer with tunable electrostatic coupling, in order to measure micro-displacements induced stiffness perturbations. Enhanced sensitivity is achieved based on the principle of energy localization in eigenvalue veering phenomena, resulting from a symmetry breaking in coupled resonator systems. Experimental results from a coupled two-resonators MEMS device are compared with both analytical calculations, and Simulink model simulations. Modal vector sensitivity is shown to be an order of magnitude higher than resonant frequency sensitivity under ambient conditions. Decreasing the coupling strength between the two resonators, using tunable electrostatic spring, is shown to enhance sensitivity, albeit in a narrowed range of perturbations.

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.

Shaped combs and parametric amplification in inertial MEMS sensors

In this paper slope-shaped comb are designed, modeled and experimentally verified for electrostatic parametric amplification of micro-displacements. Parametric resonance techniques applied to inertial sensors requires a periodic stiffness modulation, as expressed by the Mathieu equation, and is generally implemented using gap-varying and non-overlapping combs. MEMS vibratory gyroscopes provide the opportunity for applying parametric amplification for both the sensing and driving modes. While gap-varying combs are efficiently used for small displacements (e.g. the sensing mode), larger displacements require a different approach, where slope-shaped combs are a good alternative. Analytical model of slope shaped combs is carried out and compared with experimental characterization of fabricated devices. The analytical model predicts a spring modulation of 0.1%-0.65% spring modulation for 10 V to 40V applied common mode DC bias, and it can be increased for steeper slopes. The parametric amplification operation was experimentally tested using linear differential voltage actuation on the area-varying combs and a phase-synchronised common mode voltage (using a PXIe 1062Q DAQ controller) applied to the left and right shaped combs.

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

AbstractWe propose a novel adaptive technique based on pseudo-random (PN) sequences for self-calibration and self-testing of MEMS-based inertial sensors (accelerometers and gyroscopes). The method relies on using a parameterized behavioral model implemented on FPGA, whose parameters values are adaptively tuned, based on the response to test pseudo-random actuation of the physical structure. Dedicated comb drives actuate the movable mass with binary maximum length pseudo-random sequences of small amplitude, to keep the device within the linear operating regime. The frequency of the stimulus is chosen within the mechanical spectral operating range of the micro-device, such that the induced response leads to the identification of the mechanical transfer function, and to the tuning of the associated digital behavioral model. In case of a micro-gyroscope, experimental results demonstrate the adaptive tracking of the damping coefficient from 5.57 × 10−5  Kg/s to 7.12 × 10−5  Kg/s and of the stiffness coefficient from 132 N/m to 137.7 N/m. In the case of a MEMS accelerometer, the damping and stiffness coefficients are correctly tracked from 3.4 × 10−3  Kg/s and 49.56 N/m to 4.57 × 10−3  Kg/s and 51.48 N/m, respectively—the former values are designer-specified target values, while the latter are experimentally measured parameters for fabricated devices operating in a real environment. Hardware resources estimation confirms the small area the proposed algorithm occupies on the targeted FPGA device.

High sensitivity accelerometer operating on the border of stability with digital sliding mode control

We propose a novel sliding mode control (SMC), implemented on FPGA, for robust stabilization and high sensitivity operation of a capacitive micro-accelerometer on the pull-in border of stability. SMC feedback enforces a maximum sensitivity position of the proof mass in a normally unstable equilibrium, while the value of the external acceleration is extracted from the “chattering” noise bitstream produced by the controller action. Experimental results show a stabilization of the proof mass at the pull-in position and a measurement of small accelerations. The proposed technique, implemented in LabView and FPGA hardware, represents a promising alternative for the digital control of capacitive MEMS devices, with a lighter implementation footprint than common sigma-delta approaches used for inertial sensors.