Robust Testing of Cellular Neural Networks.

Abstract

A method for detecting circuit faults within two dimensional Cellular Neural Network (CNN) arrays is presented. The need to develop robust methods for detecting faults is driven by the lack of visibility of the internal nodes in VLSI implementations of CNN arrays. The method is composed of both behavioral and parametric tests and detects faults independent of the size or topology of the CNN array. The behavioral tests reveal nodes that exhibit opened, shorted, or stuck-at a supply voltage faults. The parametric tests reveal excessive time constant mismatches, impedance mismatches and voltage offsets. Seven fault cases are introduced into a macromodel of a voltage mode CNN array to provide insight to the usefulness of the proposed testing methodology.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1995
Accession Number
ADA291219

Entities

People

  • Jose Pineda De Gyvez

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Change Detection
  • Detection
  • Dynamic Tests
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Impedance
  • Molecular Mechanics Methods
  • Neural Networks
  • Resistance
  • Simulations
  • Simulators
  • Static Tests
  • Test Methods
  • Topology
  • Two Dimensional
  • Universities

Fields of Study

  • Engineering

Readers

  • Integrated Circuit Design and Technology.
  • Microwave Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks