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This paper reports results of an ongoing research on MOSFET parameter extraction using the EKV model. This work continues efforts in finding the best method for efficient and robust parameter extraction based on voltage-current structure characteristics. In the extraction process, voltage-current characteristics are matched by the characteristics generated by the model. Values of parameters for which the best match is observed are the result of the extraction. The extraction process is considered an optimization problem, which is then solved by an evolutionary algorithm followed by the Nelder-Mead simplex. The influence of the measurement error on the extraction results is investigated experimentally.
In this paper we report an on going research on applying evolutionary computation to the identification of technological parameters of MOS transistors (MOSFETs) using the current-voltage measurements. The identification consists in approximating the observed values of the current with the values generated by the transistor model. Values of parameters for which the smallest approximation error is observed are assumed to be the best estimations to the real values. The model is nonlinear and nondifferentiable, and the error function takes multiple local minima with respect to the parameter values. We apply a combination of an evolutionary algorithm together with the Nelder-Mead method to minimize the error function and we experimentally investigate the effectiveness of the proposed approach.
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