Modeling the sensitivity of capacitive pressure sensors with micro-structured wavy surfaces

In recent decades, capacitive pressure sensors (CPSs) with high sensitivity have demonstrated significant potential in applications such as medical monitoring, artificial intelligence, and soft robotics. Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization, with surface microstructures such as wrinkles, pyramids, and micro-pillars proving effective. Although finite element modeling (FEM) has guided enhancements in CPS sensitivity across various surface designs, a theoretical understanding of sensitivity improvements remains underexplored. This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics. These theoretical insights are corroborated by FEM and experimental validations. Our findings underscore that optimizing material properties, such as Young’s modulus and relative permittivity, alongside adjustments in surface roughness and substrate thickness, can significantly elevate the sensitivity. The optimal performance is achieved when the amplitude-to-wavelength ratio (H/λ) is about 0.2. These results offer critical insights for designing ultrasensitive CPS devices, paving the way for advancements in sensor technology.