Title :
Fault detection of analog circuits using neural networks and Monte-Carlo analysis
Author :
Ashouri, Mohammad-Reza
Author_Institution :
Dept. of Electr. & Comput. Eng., Isfahan Univ. of Technol., Iran
Abstract :
A new fault detection technique for analog circuits is developed. In this method, the circuit is supplied with a ramp shape voltage. The resulting supply current is analysed with a new unsupervised neural network. Simulating different faults and the Monte-Carlo analysis to account for parametric change and tolerances implements the training of the proposed neural network
Keywords :
Monte Carlo methods; analogue integrated circuits; fault diagnosis; integrated circuit testing; network parameters; neural nets; unsupervised learning; Monte Carlo analysis; analog circuits; fault detection; neural networks; parametric change; ramp shape voltage; tolerances; unsupervised neural network; Analog circuits; Circuit faults; Circuit simulation; Circuit testing; Current supplies; Electrical fault detection; Neural networks; Pattern analysis; Power supplies; Voltage;
Conference_Titel :
Circuits and Systems, 2001. MWSCAS 2001. Proceedings of the 44th IEEE 2001 Midwest Symposium on
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-7150-X
DOI :
10.1109/MWSCAS.2001.986284
