System Theoretic Models for High Density VLSI Structures

Abstract

This research project involved the development of mathematical models for analysis, synthesis, and simulation of large systems of interacting devices. The work was motivated by problems that may become important in high density VLSI chips with characteristic feature sizes less than 1 micron: it is anticipated that interactions of neighboring devices will play an important role in the determination of circuit properties. It is hoped that the combination of high device densities and such local interactions can somehow be exploited to increase circuit speed and to reduce power consumption. To address these issues from the point of view of system theory, research was pursued in the areas of nonlinear and stochastic systems and into neural network models. Statistical models were developed to characterize various features of the dynamic behavior of interacting systems. Random process models for studying the resulting asynchronous modes of operation were investigated. The local interactions themselves may be modeled as stochastic effects. The resulting behavior has been investigated through the use of various scaling limits, and by a combination of other analytical and simulation techniques. Techniques arising in a variety of disciplines where models of interaction have been formulated and explored were considered and adapted for use.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA209901

Entities

People

  • Anastasios Vergis
  • Anthony Kuh
  • Bradley W. Dickinson
  • Kenneth Steiglitz
  • Thomas Petsche
  • William E. Hopkins Jr.

Organizations

  • Princeton University

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata
  • Computational Science
  • Computer Programming
  • Computers
  • Content Addressable Memory
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • High Density
  • Information Science
  • Information Systems
  • Mathematical Filters
  • Mathematical Models
  • Neural Networks
  • Probabilistic Models
  • Random Variables

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms