A modified MODPEX model for MOSFET and parameter optimization using excel-based genetic algorithm
MODPEX is widely used in industry and universities to produce Pspice model for power semiconductor devices, including MOSFETs. However, the model generated by MODPEX for MOSFETs does not include channel charge at strong inversion condition. This paper presents a modified MODPEX model for MOSFETs, which includes channel charge at strong inversion condition. A process based on genetic algorithm (GA) implemented in Microsoft Office Excel is developed to optimize parameters of the modified MODPEX model. Static and dynamic validation proves that the modified MODPEX model improves simulation accuracy in both switching loss and driver loss.
Application of the Genetic Algorithm in MOSFET Modeling and Parameter Extraction
In this paper, the genetic algorithm is introduced into parameter optimization for MOSFET small-signal model parameter extraction for up to 60 GHz millimeter-wave operations. Initial parameters of the model are extracted by curve fitting and direct parameter extraction method in saturation region approximately, and then fourteen independent parameters of the equivalent circuit model are optimized together. The real and imaginary part of S-parameter are set as the fitting targets simultaneously in this study. With the more convenient genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the initial parameters without optimization.
Parameter extraction for PSP MOSFET model using particle swarm optimization
This paper demonstrates the application of particle swarm optimization (PSO) for parameter extraction of MOSFET model for the first time. Parameters are extracted for PSP MOS model for 65 nm technology NMOS devices. It has been shown that the performance of the PSO algorithm is better than the genetic algorithm (GA) in terms of accuracy and consistency.