A coupled FDTD-artificial neural network technique for large-signal analysis of microwave circuits

✍ Scribed by S. Goasguen; S. M. El-Ghazaly

• Publisher: John Wiley and Sons
• Year: 2001
• Tongue: English
• Weight: 302 KB
• Volume: 12
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/mmce.10018

✦ Synopsis

We propose a first-order global modeling approach of Monolithic Microwave Integrated Circuits (MMIC) by modeling the active device with a neural network based on a full hydrodynamic model. This neural network describes the nonlinearities of the equivalent circuit parameters of an MESFET implemented in an extended Finite Difference Time Domain mesh to predict large-signal behaviors of the circuits. We successfully represented the transistor characteristics with a one-hidden-layer neural network, whose inputs are the gate voltage V gs and the drain voltage V ds . The trained neural network shows excellent accuracy and dramatically reduces the computational time in comparison with the hydrodynamic model. Small-signal simulation is performed and validated by comparison with HP-Libra. Then large-signal behaviors are obtained, which demonstrates the successful use of the artificial neural network.