Fuzzy-neural tool for topology extraction of RF and microwave transistors

Today, the increasing need for advanced high-frequency communication technologies leads to a continuous development of new and more complex models for active devices such as RF and microwave transistors [1]. Parameter extraction for small-signal electrical equivalent circuit modeling has been deeply studied and numerous techniques have been developed. However, it is still difficult to determine all the small-signal model elements with a high degree of certainty.;In this thesis, an efficient tool is presented for transistor model extraction and characterization. Fast and accurate, the adopted technique couples neural networks and fuzzy theory to extract the most suitable small-signal equivalent circuit topology of RF/microwave field effect and heterojunction bipolar transistors. By combining the Fuzzy c-means method [2] and the neural representation of a transistor behavior [3], the small-signal equivalent circuit parameters are efficiently evaluated through a fuzzy-neural network, based on the selection of the most suitable circuit topology of the active device.