Linear dynamic space mapping approach for large-signal statistical modeling of microwave devices

This thesis presents a novel technique for large-signal statistical modeling of nonlinear microwave devices. A new statistical space mapping concept is introduced that can expand a large-signal nominal model into a large-signal statistical model. The nominal model is an accurate large-signal model developed from one complete large-signal measurement and it describes the nominal performance of the device population. The mapping contains the statistical parameters estimated by fitting the DC and bias-dependent S-parameter data of the device population. In this way, the nominal model mainly represents the large-signal nonlinear behavior of the device population while the random variations around the nominal model are represented by the space mapping functions. It helps to efficiently develop large-signal statistical models while reducing the expense of otherwise massive large-signal measurements for many devices. Examples of MESFET and HEMT statistical modeling demonstrate that the technique can approximate the large-signal statistical characteristics using only one set of large-signal data. The use of such statistical model in amplifier yield design further demonstrates the capability of the technique in capturing the large-signal statistical properties.