Finite element analysis of a high-sensitivity capacitive sensor with varied geometry of dielectric media

The current study utilizes the Finite Element Method (FEM) approach to study the variation in pressure sensitivity and capacitance of the sensor consisting of varied geometry of the intermediate dielectric layers made up of Polydimethylsiloxane (PDMS). Two configurations of geometries - cubic column and cylindrical column sandwiched between two flat layers are analyzed. In addition to the change in the geometry, the thickness of the intermediate layer is also varied and analyzed. During analysis, the total surface contact area between the intermediate layer and the flat layers is kept the same for both geometries. It is observed that the change in the geometry of the intermediate dielectric layer has a significant effect on the pressure sensitivity and capacitance of the sensor. The cylindrical column structure yields enhanced pressure sensitivity while the cubical column structure is found to yield enhanced capacitance. Stress analysis of the capacitive model reveals that cylindrical structures exhibit better sensitivity at 200kPa pressure as compared to cube structures, with an improvement of 27.6% displacement. Furthermore, an increase in the thickness of the intermediate dielectric media results in enhanced sensitivity of the capacitor model, with cylindrical structures showing a 3.6 times higher sensitivity as the layer thickness varies. The temperature nonlinearity study shows the variation of capacitance and sensitivity with respect to temperature is diminishing rate of decrease, indicating a nonlinear trend. Comparison between Finite Element (FE) and analytical results indicates close agreement in both capacitance and displacement of the model. Thus, the proposed model demonstrates that the geometry of dielectric media, as well as the thickness of the model, and temperature nonlinearity significantly influence the analysis of capacitive sensors. These findings emphasize the critical role of geometry design in optimizing sensor performance, offering valuable insights for the advancement of sensor technology.