Multi-bias decomposition-based optimisation for the extraction of small-signal GaAs FET models

[摘要] ENGLISH ABSTRACT: The availability of accurate nonlinear models is essential for the accurate and cost effectivedesign of high frequency telecommunication systems. A key step in the creation of such modelsis the extraction of small signal models from measured s-parameter data.This thesis describes the development and evaluation of a new extraction algorithm. The methodmakes use of a decomposition-based optimiser which divides the problem into subproblems thatare solved separately. The procedure is accurate, suited for handling large problems, and startingvalue independent. The success of the algorithm is con finned using a variety of tests. Bothsimulated and measured data for a GaAs MESFET and pHEMT are used. The test results alsoshow the limitations of single bias extractions. In single bias extractions, the data measured atdifferent bias points are handled as separate extractions.It is shown that the decomposition-based optimiser can be used to create a new multi-biasextraction algorithm. The multi-bias procedure combines the s-parameters measured at differentbias points into one extraction problem, and defines the extrinsic model elements to be biasindependent. This enforces a degree of physical reality onto the extraction. Tests prove thealgorithm to be robust and accurate. It is able to find previously difficult to detennine elementswith a high degree of precision. Results are presented that indicate how the extractionuncertainty decreases as the number of bias points used in the multi-bias extraction is increased.The new algorithm produces small signal models that accurately represents the measured s-parametersat all bias points.

[发布机构] Stellenbosch University