Accession Number:

ADA291219

Title:

Robust Testing of Cellular Neural Networks.

Descriptive Note:

Technical rept.,

Corporate Author:

TEXAS A AND M UNIV COLLEGE STATION DEPT OF ELECTRICAL ENGINEERING

Personal Author(s):

Report Date:

1995-02-01

Pagination or Media Count:

19.0

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.

Subject Categories:

  • Computer Hardware
  • Computer Systems
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE