A Combined State Space Formulation/Equivalent Circuit and Neural Network Technique for Modeling of Embedded Passives in Multilayer Printed Circuits

Abstract

In this paper, we present a new approach for modeling the high-frequency effects of embedded passives in multilayer printed circuits, utilizing state space equations or equivalent circuit together with neural network techniques. In this approach, the neural network based model structure is trained using full wave electromagnetic (EM) data. The resulting embedded passive models are accurate and fast, can be used in both frequency/time domain simulators. Examples of embedded resistor and capacitor models demonstrate that the combined model can accurately represent EM behavior in microwave/RF circuit design. In high-level circuit design, we applied our combined EM based neural models for signal integrity analysis and design of multilayer circuit to illustrate that the geometrical parameters can be continuously adjusted by using neural network techniques. Optimization and Monte-Carlo analysis are performed showing that the combined models can be efficiently used in place of computationally intensive EM models of embedded passives to speed up circuit design.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADP014208

Entities

People

  • Jingjun Xu
  • M. C. Yagoub
  • Q. J. Zhang
  • X. Ding

Organizations

  • Carleton University

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer-Aided Design
  • Electrical Engineering
  • Electronics
  • Engineering
  • Equivalent Circuits
  • Frequency Domain
  • Geometry
  • Information Systems
  • Neural Networks
  • Printed Circuits
  • Signal Processing
  • Simulations
  • Simulators
  • Systems Engineering
  • Three Dimensional
  • Time Domain

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space