Modeling power and intermodulation behavior of microwave transistors with unified small-signal/large-signal neural network models

✍ Scribed by F. Giannini; G. Leuzzi; G. Orengo; P. Colantonio

• Publisher: John Wiley and Sons
• Year: 2003
• Tongue: English
• Weight: 260 KB
• Volume: 13
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/mmce.10091

✦ Synopsis

This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large-signal model of a power MESFET device, modeling the nonlinear relationship of drain-source current I ds as well as gate and drain charge Q g and Q d with respect to intrinsic voltages V gs and V ds over the whole operational bias region. The neural models have been implemented into a user-defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load-pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first-order derivative information.