By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

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.

Published in:

2007 International Symposium on Signals, Systems and Electronics

Date of Conference:

July 30 2007-Aug. 2 2007