An extrinsic component parameter extraction method for high power RF LDMOS transistors

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. © 2008 IEEE

A nonlinear electro-thermal scalable model for high-power RF LDMOS transistors

A new nonlinear charge-conservative scalable dynamic electro-thermal compact model for laterally defused MOS (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 deembedding 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, 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. Large-signal validation of this new model shows a very good agreement with measurements at 2.14 GHz. © 2008 IEEE

A nonlinear electro-thermal scalable model for high-power RF LDMOS transistors

A new nonlinear charge-conservative scalable dynamic electro-thermal compact model for laterally defused MOS (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 deembedding 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, 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. Large-signal validation of this new model shows a very good agreement with measurements at 2.14 GHz. © 2008 IEEE

An extrinsic component parameter extraction method for high power

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