NDLI: State-Space Dynamic Neural Network Technique for High-Speed IC Buffer Modeling

Content Provider	IEEE Xplore Digital Library
Author	Yi Cao Qi-Jun Zhang
Copyright Year	2007
Description	Author affiliation: Carleton Univ., Ottawa (Yi Cao; Qi-Jun Zhang)
Abstract	Artificial neural networks (ANN) have been recently recognized as useful tools for RF/microwave modeling and design. In this paper, a recent state-space dynamic neural network (SSDNN) approach for transient behavior modeling of high-speed nonlinear circuit is summarized. This technique extends the existing dynamic neural network (DNN) approach into a more generalized and robust state-space formulation. A training algorithm exploiting the adjoint sensitivity computation is utilized to enable SSDNN to efficiently learn from the transient input and output waveform data without relying on the circuit internal details. Through an exact circuit representation, the trained SSDNN model can be conveniently implemented and used in SPICE-like circuit simulators. We also review a set of stability criteria for checking local and global stabilities of the SSDNN model. An example of SSDNN modeling of physics-based high-speed driver circuit is presented. It's demonstrated that the SSDNN model can offer fast and accurate transient responses for high-speed interconnect design.
Starting Page	71
Ending Page	74
File Size	341621
Page Count	4
File Format	PDF
ISBN	1424414482
DOI	10.1109/ISSSE.2007.4294416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-30
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Integrated circuit modeling Neural networks High speed integrated circuits Artificial neural networks Stability criteria Radio frequency Nonlinear circuits Robustness Computational modeling Circuit simulation transient analysis modeling stability criteria nonlinear circuits
Content Type	Text
Resource Type	Article

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