ANN-based estimation of MEMS diaphragm response: An application for three leaf clover diaphragm based Fabry-Perot interferometer

In this study, an artificial neural network (ANN) based model is developed for MEMS diaphragm analysis, which does not require difficult and time-consuming FEM processes. ANN-based estimator is generated for static pressure response (d) and dynamic pressure response (f) analysis of TLC (three leaf clover) diaphragms for Fabry-Perot interferometers as an example. TLC is one of the unsealed MEMS design diaphragms formed by three leaves of equal angles. The diaphragms used to train ANNs are designed with SOLIDWORKS and analyzed with ANSYS. A total of 1680 TLC diaphragms are simulated with eight diaphragm parameters (3 for SiO2 material, 4 for geometry, and 1 for pressure) to create a data pool for ANN's training, validation, and testing processes. 80% of the data is used for training, 15% for validation, and the remaining for testing. Only four geometric parameters are used as input in the ANN estimator, and the material parameters are added to the model with an analytical multiplier. Thus, network models that estimate d and f values for all kinds of diaphragm materials are proposed, with a material-independently trained ANN structure. The performance of the ANN model is compared with the empirical equation suggested in the literature, and its superiority is demonstrated. In addition, the d and f parameters of TLC diaphragms designed with five different materials (Si, In2Se3, Ag, EPDM, Graphene) are estimated to be very close to the real ones. By using the proposed method, analyses of TLC diaphragms are quickly performed without the need for time-consuming and costly design and analysis programs. © 2022 Elsevier Lt

A Novel High-Performance Beam-Supported Membrane Structure with Enhanced Design Flexibility for Partial Discharge Detection

A novel beam-supported membrane (BSM) structure for the fiber optic extrinsic Fabry-Perot interferometer (EFPI) sensors showing an enhanced performance and an improved resistance to the temperature change was proposed for detecting partial discharges (PDs). The fundamental frequency, sensitivity, linear range, and flatness of the BSM structure were investigated by employing the finite element simulations. Compared with the intact membrane (IM) structure commonly used by EFPI sensors, BSM structure provides extra geometrical parameters to define the fundamental frequency when the diameter of the whole membrane and its thickness is determined, resulting in an enhanced design flexibility of the sensor structure. According to the simulation results, it is noted that BSM structure not only shows a much higher sensitivity (increased by almost four times for some cases), and a wider working range of fundamental frequency to choose, but also an improved linear range, making the system development much easier. In addition, BSM structure presents a better flatness than its IM counterpart, providing an increased signal-to-noise ratio (SNR). A further improvement of performance is thought to be possible with a step-forward structural optimization. The BSM structure shows a great potential to design the EFPI sensors, as well as others for detecting the acoustic signals