A Neuro-Space Mapping Method for Harmonic Interference Prediction of SOIFET Radio Frequency Switches

With the rapid development of wireless communication, the requirements of high-integration and low-cost radio frequency (RF) front-end modules in mobile phones result in more usage of silicon-on-insulator field effect transistors (SOIFETs) for the manufacturing of RF switches. However, the nonlinear behavior of SOIFET switches in off -state always produces unwanted harmonics distortion interference when they are excited by a large signal. In this article, we simplify the well-known physics-based surface potential model to an interelectrode nonlinear capacitance (INC) model since it adequately describes the harmonic effects produced by the transistor. The INC model, referred to as the coarse model, cannot match the behavior of the real switch, since many parameters of the switch cannot be accurately determined. This article proposes a novel dynamic neuro-space mapping network model, referred to as the fine model, to optimize the INC model. The proposed model takes advantage of the high accuracy of the fine model and the fast speed of the coarse model. Ultimately, the proposed method can accurately predict the harmonics interference for SOIFET switches in the complex RF front-end environment and provides an intuitive guideline under the design stage to prevent EMI problems.