Gallium Arsenide Mesfet Small-Signal Modeling Using Backpropagation & RBF Neural Networks

• Other Authors: Langoni, Diego (authoraut)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Electrical engineering; Computer engineering
• Online Access: http://purl.flvc.org/fsu/fd/FSU_migr_etd-3286

The small-signal intrinsic ECPs (equivalent circuit parameters) of a 4x50 µm gate width, 0.25 µm gate length GaAs (gallium arsenide) MESFET (metal semiconductor field-effect transistor) were modeled versus bias (voltage and current) and temperature using backpropagation and RBF (radial basis function) ANNs (artificial neural networks). The resulting ANNs consisted of 3-input, 8-output models of the MESFET ECPs and were compared to each other in terms of memory usage, convergence speed, and accuracy. Also, each network's performance was evaluated under "normal" training conditions (75% training data with a uniform distribution) and "stressed" training conditions (50% and 25% training data with a uniform distribution, 75%, 50%, and 25% training data with a skewed distribution). The results showed that for the RBF network, much better overall convergence speed as well as better accuracy under both "normal" and "moderately stressed" training conditions were obtained. However, the backpropagation network yielded better accuracy for the "extremely stressed" training conditions and better overall memory usage. === A Thesis submitted to the Electrical and Computer Engineering Department in partial fulfillment of the requirements for the degree of Master of Science. === Fall Semester, 2005. === October 19, 2005. === MESFET Modeling, RBF, Backpropagation, Artificial Neural Networks, Intrinsic Ecps === Includes bibliographical references. === Mark H. Weatherspoon, Professor Directing Thesis; Anke Meyer-Bäse, Committee Member.