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Detecting variations of small‐signal equivalent‐circuit model parameters in the Si/SiGe HBT process with ANN
Author(s) -
Taher H.,
Schreurs D.,
Gillon R.,
Vestiel E.,
van Niekerk C.,
Alabadelah A.,
Nauwelaers B.
Publication year - 2005
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.20056
Subject(s) - heterojunction bipolar transistor , sensitivity (control systems) , equivalent circuit , artificial neural network , process (computing) , electronic engineering , limit (mathematics) , signal (programming language) , microwave , computer science , optoelectronics , materials science , engineering , electrical engineering , transistor , artificial intelligence , mathematics , telecommunications , voltage , bipolar junction transistor , programming language , operating system , mathematical analysis
To capture variations in the Si/SiGe HBT process characteristics, we could extract a complete equivalent‐circuit model for each device, but this would be a time‐consuming process. In this article, we develop an alternative approach based on an artificial neural network (ANN). To keep the complexity of the ANN low, we limit this mapping to the most sensitive elements by utilizing sensitivity analysis on a reference device. The results show that the ANN predicts the model parameters very well. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.