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

Abstract

An integrated multibias extraction technique for MESFET and high electron-mobility transistor (HEMT) models is presented in this paper. The technique uses-parameters measured at various bias points in the active region to construct 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-independent elements. This problem is solved by an extremely robust decomposition-based optimizer, which splits the problem into subproblems, being the number of unknowns. The optimizer consistently converges to the same solution from a wide range of randomly chosen starting values. No assumptions 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 the values of the model elements and a decrease in the extraction uncertainty as the number of bias points in the extraction is increased. Robustness tests using 100 extractions, each using a different set of random starting values, are performed on measured-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 successfully, leading to the simultaneous determination of 342 model elements.

Prediction of IMD in LDMOS transistor amplifiers using a new large-signal model

“…A simple model topology, shown in Fig. 5, has been used, where the parasitic and intrinsic small-signal parameter values have been found for various bias voltages using a robust multibias extraction technique [14].…”

Fuzzy neural-based approaches for efficient RF/microwave transistor modeling

“…Since FETs and HBTs are widely used in RF/microwave communication systems, a large number of modeling approaches have been proposed [14,[27][28][29][30]. Detailed physics-based transistor models are accurate but slow.…”

“…Nevertheless, they are difficult to develop, equivalent circuit models remain the most used modeling approach, where the element values for a FET, for instance, can be determined either by direct extraction [27] or by optimization-based extraction [28]. Fast and simple to implement direct-extraction techniques provide adequate values for the more dominant circuit model elements, but they cannot determine all the extrinsic elements uniquely [12,14]. On the other side, optimization-based extraction techniques are more accurate but computationally intensive and relatively sensitive to the choice of starting values.…”

“…However, they present two serious shortcomings in transistor equivalent circuit characterization. First, the transistor equivalent circuit parameters are not unique and second, the nonideal effects are often not modeled adequately [12][13][14]. These issues may lead to inadequate solutions when using neural-based transistor models in circuit design and optimization, particularly in the presence of uncertainties/noise in training data.…”

Artificial bee colony algorithm for small signal model parameter extraction of MESFET

“…In the recent past, different techniques (Yaser, 2000;Van Niekerk et al, 2000) have been reported in the literature for extracting the model parameters of MESFET. These techniques are normally based on either analytical or numerical optimization techniques.…”