Statistical Space Mapping Approach for Large-Signal Nonlinear Device Modeling
An efficient nonlinear statistical modeling technique for microwave devices is presented. 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 extracted or trained from one complete set of large-signal data. The statistical property is achieved by a dynamic mapping between the behavior of the nominal model and that of the statistical samples of a given population of devices. The parameters in the mapping, which are statistical parameters, can be extracted from DC and small-signal S-parameter data of many device samples. Example of a MESFET device modeling and its use in statistical design of a three-stage amplifier circuit demonstrate that the statistical space-mapped model can approximate the large-signal statistical characteristics using only one set of large-signal data. It helps to efficiently develop large-signal statistical models while reducing the expense of otherwise massive large-signal measurements for many devices