New Approach of Test for DAC Using Fuzzy Neural Networks

Author

Soumi, Mohammad Ma ; Mohammadi, Karim

Author_Institution

KULeuven, Leuven

fYear

2007

fDate

20-24 Oct. 2007

Firstpage

47

Lastpage

51

Abstract

Due to the existence of analogue signals, testing the mixed signal circuits is a complex and complicated one. D/A converters are one of the most important types of these circuits. In this paper, a method is presented to determine the points where faults are occurred in a 4-bit resistive ladder D/A converter, using fuzzy rules. For the purpose of implementing fuzzy rules, neural networks have been utilized. Also for improvement the fault coverage of this test method the LVQ neural network has been used. Firstly, the circuit has been simulated using ORCAD9 and then training patterns have been elicited. Continually, network simulation and training have been implemented via MATLAB6.1trade.

Keywords

circuit testing; digital-analogue conversion; electronic engineering computing; fuzzy neural nets; D-A converters; analogue signals; fuzzy neural network; fuzzy rules; mixed signal circuits; Built-in self-test; Circuit faults; Circuit simulation; Circuit testing; Electronic equipment testing; Fault detection; Fuzzy neural networks; Mathematical model; Neural networks; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on

Conference_Location

Rio de Janeiro

Print_ISBN

978-0-7695-2976-9

Type

conf

DOI

10.1109/ISDA.2007.78

Filename

4389584