Design parameters modeling for RF MEMS phase shifter based on artifical neural network

A modeling arithmetic based on design parameters for MEMS phase shifter working in millimeter wave is presented. Three crucial design parameters were selected as modeling objects. The HFSS software was used to obtain the data for training and valuing the three different neural networks which modeled the S and design parameters of the phase shifter. The experiments show that compared with HFSS simulation results, the modeling method presented in paper for the center frequence, wideband and minimum insertion loss (33~37 GHz) of the phase shifter had mean absolute error of 0.094~0.171 GHz, 0.085~0.159 GHz and 0.040~0.048 dB respectively. The accuracy of model has been improved at least 50% comparing to the method based on S parameters.