Modelling of a MEMS switch for high-speed sampling circuits using artificial neural network perspective

In designing and developing high-speed digital circuits it is important to develop a switch on a transmission line having higher phase velocity, low insertion loss and high isolation. An electro-statically actuated Radio Frequency Micro Electro Mechanical System (RF MEMS) switch realized on Elevated Coplanar Waveguide (ECPW) platform is fabricated and tested for RF characteristics. The introduced MEMS switch is capable of accomplishing high speed since it does not include a transistor in its structure. In this study, a 20 GHz ECPW based RF MEMS switch is designed, fabricated and characterized. Comparing the ECPW switch to normal CPW switches, the pull-in voltage is substantially lower and it is about 2–2.8 V. This decreased pull-in lowers the time required to change between states and reveals the usage of these switches for high-speed digital circuitry. At 20 GHz, measured RF performance reveals an insertion loss of − 2.7 dB and an isolation of − 5 dB. Parametric study and optimization need to be carried out to improve the isolation of the proposed switch. This paper gives an overview of the applicability of Artificial Neural Networks (ANN) in modelling a better ECPW-based RF MEMS switch. ANN Models are used to determine the required switch dimensions to obtain better radio frequency characteristics. ANN approach has been used to overcome time-consuming optimization methods for determining the radio frequency parameters in EM simulators like HFSS. In this work, the proposed ANN model reduces the switch’s design time by 98.9862% compared to EM simulator HFSS, which takes approximately 39 min and 27 s.