Improvement of Capacitive Accelerometers: Integrating Modeling, Electrode Design, Signal Processing, and Material Selection

Abstract

Abstract This work offers a thorough method for integrating advanced modeling, electrode design, signal processing, and material selection techniques to maximize the performance of capacitive accelerometers. Key performance measures, including noise reduction, sensitivity, and range, can be systematically analyzed and simulated to forecast how design decisions would affect the overall behavior of the sensor. Advanced electrode designs greatly increase capacitance, which results in more precise and trustworthy sensor readings. These designs include optimizing the shape and using high-permittivity materials. Furthermore, to guarantee that the accelerometer's output precisely represents actual motion even in noisy surroundings, noise reduction strategies including filtering and digital signal processing methods are essential. Additionally, calibration is emphasized as a critical step in preserving measurement accuracy over time and accounting for environmental changes and sensor drift. The choice of material, with an emphasis on thermally stable and high-permittivity materials, is crucial in determining the capacitance, sensitivity, and durability of the sensor. The study offers a paradigm for the creation of capacitive accelerometers that perform better across a variety of applications by striking a balance between these variables.