Robust Testing of Cellular Neural Networks.
José Pineda de Gyvez · 1995
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.