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.
Parameter extraction for the PSP MOSFET model by the combination of genetic and Levenberg-Marquardt algorithms
Based on the combination of the genetic and Levenberg-Marquardt algorithms, a new method is developed to perform both local and global parameter extraction for the PSP MOSFET model. It has been successfully used to extract parameter sets for a 65-nm technology node. Numerical examples demonstrate its ability to obtain highly accurate model parameter values without excessive computational cost.
A hybrid genetic algorithm for MOSFET parameter extraction
New transistor models are becoming more complex to accommodate the new effects introduced by shrinking channel length and new fabrication structures. Conventional parameter extraction techniques perform poorly when applied to these new models. In this paper, a new hybrid evolutionary algorithm is adopted in order to address the problems normally encountered in conventional algorithms and to obtain accurate parameter values. The algorithm relies on applying genetic algorithms (GA) in order to reach a near-optimal solution then a conventional least squares optimization process is applied to find the optimal parameter set. The proposed algorithm has outperformed both conventional parameter extraction techniques and pure GA-based ones. The proposed hybrid algorithm was tested on the available data for different 12 NMOS devices of different gate lengths and widths for the 0.35 mm technology. The evolutionary algorithm resulted in RMS fitting errors in the range form 0.5% to 1.5% compared to a value of 4% error when conventional parameter extraction techniques were applied.