A robust integrated multibias parameter-extraction method for MESFET and HEMT models

An integrated multibias extraction technique for MESFET and high electron-m obility transistor (HEMT) models is presented in this paper. The technique uses s-parameters measured at various bias points in the active region to c onstruct one optimization problem, of which the vector of unknowns contains a set of bias-dependent elements for each bias point and one set of bias-i ndependent elements. This problem is solved by an extremely robust decompos ition-based optimizer, which splits the problem into n subproblems, n being the number of unknowns. The optimizer consistently converges to the same s olution from a wide range of randomly chosen starting values. No assumption s are made concerning the layout of the device or the bias dependencies of the intrinsic model elements. It is shown that there is a convergence in th e values of the model elements and a decrease in the extraction uncertainty as the number of bias points in the extraction is increased. Robustness te sts using 100 extractions, each using a different set of random starting va lues, are performed on measured s-parameters of a MESFET and pseudomorphic HEMT device. Results indicate that the extracted parameters typically vary by less than 1%. Extractions with up to 48 bias points were performed succe ssfully, leading to the simultaneous determination of 342 model elements.