Abstract-A new nonlinear, charge-conservative, scalable, dynamic electro-thermal compact model for LDMOS RF power transistors is described in this paper. The transistor is characterized using pulsed I-V and S-parameter measurements, to ensure isothermal conditions. A new extrinsic network and extrinsic parameter extraction methodology is developed for high power RF LDMOS transistor modeling, using manifold de-embedding by electromagnetic simulation, and optimization of the extrinsic network parameter values over a broad frequency range. The intrinsic model comprises controlled charge and current sources that have been implemented using artificial neural networks (ANNs), designed to permit accurate extrapolation of the transistor's performance outside of the measured data domain. A thermal sub-circuit is coupled to the nonlinear model. Largesignal validation of this new model shows a very good agreement with measurements at 2.14 GHz.

Abstract-A new extrinsic network and extrinsic parameter extraction methodology is developed for high power RF LDMOS transistor modeling. This new method uses accurate manifold deembedding using electromagnetic simulation, and optimization of the extrinsic network parameter values over a broad frequency range. The new extrinsic network accommodates feedback effects which are observed in high power transistors. This improved methodology allows us to achieve a good agreement between measured and modeled S-parameters in the frequency range of 0.5 to 6 GHz for different bias conditions. Large-signal verification of this new model shows a very good match with measurements at 2.14 GHz.

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This paper presents an optimization-based technique to develop silicon substrate for accurate and efficient electromagnetic (EM) simulations. The proposed method simplifies the highly nonlinear substrate doping profile into a few regions with effective conductivities. The accuracy of the optimized substrate is validated against measurement data for two spiral inductors. This simplified substrate enables fast and accurate EM simulations. The optimization procedure can be applied to either measurement-based or process simulation-based substrate development, and has a potential to enable EM model creation even before wafer fabrication.