Artificial Neural Network Approach to Model Sidewall Metallization of Silicon-based Bistable Lateral RF MEMS Switch for Redundancy Applications

Radio Frequency Micro Electro Mechanical System (RF MEMS) switches are rapidly evolving due to the demand for low cost, high performance, and compact communication systems. An electrothermally actuated bistable lateral MEMS switch for redundancy applications has been already fabricated and tested for mechanical characteristics. In this paper to make this switch as a suitable candidate for RF applications,sidewall metallization using gold is proposed.Modelling of sidewall metallization in Bistable Lateral RF MEMS switch using Cascade Feedforward Scaled Conjugate Gradient (CFSCG) Artificial Neural Network approach is reported. Using an inverse approach of ANN, the desired length of sidewall coating required for better RF performance in terms of return loss and insertion loss is predicted. Time-consuming optimization procedures to determine the sidewall coating in customized EM simulators such as HFSS have been overcome using the proposed CFSCG approach. In this paper, the proposed CFSCG approach reduces the design time of the switch with sidewall coating by 99.4048% compared to the conventional EM simulator which takes approximately 14 h. Validation is done by comparing the obtained results with the HFSS model and good agreement is obtained. The simulation of the proposed switch results in an insertion loss less than -1 dB in the frequency range between 1-10 GHz with gold sidewall metallization of bistable lateral RF MEMS switch.