Title :
New Approach of Test for DAC Using Fuzzy Neural Networks
Author :
Soumi, Mohammad Ma ; Mohammadi, Karim
Author_Institution :
KULeuven, Leuven
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;
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
DOI :
10.1109/ISDA.2007.78
