Resolution Enhancement of a Sigma-Delta Micro-Accelerometer Using Signal Prediction

Abstract

This paper presents a new design of a closed-loop sigmadelta accelerometer using an acceleration prediction feedback element. Thanks to a Kalman state estimator associated with a Linear-Quadratic-Gaussian (LQG) controller, the proof mass is maintained near its position of equilibrium. Consequently, operating on the relatively small proof mass displacement makes it possible to enhance the performance of the sigma-delta converter. This architecture shows an increase of more than 20 dB in signal quantization noise ratio compared to conventional sigma-delta accelerometers and has the advantage of being a completely digital solution.

A Closed-Loop <formula formulatype="inline"> <tex Notation="TeX">$\Sigma\Delta$</tex></formula> Interface for a High-Q Micromechanical Capacitive Accelerometer With 200 ng/<formula formulatype="inline"><tex Notation="TeX">$ \surd$</tex></formula>Hz Input Noise Density

“…During the charge sensing phase 2, the voltages of sensor mass and common electrode of the reference capacitors are kept at and , respectively. The output of the charge sensing is expressed as (6) The offset and low-frequency noise are canceled in the sampling phase when the switch S7 is closed. The differential output of the C&H is represented by (7) The low-noise output signal is buffered by a three-stage folded-cascode amplifier, which is used in unity gain configu- ration.…”

“…The closed-loop design offers electrical means to make the high-Q sensor element under-damped without increasing the complexity, which cannot be achieved in an open-loop configuration [3]- [5]. The modulation technique applied in analog-to-digital (AD) conversion has been widely used in a number of inertial and force sensing applications successfully, such as accelerometers and gyroscopes [6]- [9]. Closed-loop capacitive accelerometers can provide high-resolution digital output, and the electrostatic force feedback makes the sensors insensitive to the impact of process variations, which results in a more stable and accurate system.…”