Research Article
Fault Diagnosis in Analog Integrated Circuits Using Artificial Neural Networks
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 1 - Issue 27 |
| Published: February 2010 |
| Authors: A.Rathinam, R. Srinivasa Raghavan, R.Venkatraman |
10.5120/497-811
|
| PDF not available |
A.Rathinam, R. Srinivasa Raghavan, R.Venkatraman . Fault Diagnosis in Analog Integrated Circuits Using Artificial Neural Networks. International Journal of Computer Applications. 1, 27 (February 2010), 63-69. DOI=10.5120/497-811
@article{ 10.5120/497-811,
author = { A.Rathinam,R. Srinivasa Raghavan,R.Venkatraman },
title = { Fault Diagnosis in Analog Integrated Circuits Using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 1 },
number = { 27 },
pages = { 63-69 },
doi = { 10.5120/497-811 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A A.Rathinam
%A R. Srinivasa Raghavan
%A R.Venkatraman
%T Fault Diagnosis in Analog Integrated Circuits Using Artificial Neural Networks%T
%J International Journal of Computer Applications
%V 1
%N 27
%P 63-69
%R 10.5120/497-811
%I Foundation of Computer Science (FCS), NY, USA
Abstract
One of the most important tasks in design and manufacturing of integrated circuits is the testing phase. Distinguishing between faulty and fault free ICs is a difficult task Therefore, simulations are being done for different circuits to identify fault free and faulty circuits. Analog circuits like Low pass filter, High pass filter, Band pass filter, Band reject filter, State variable filter, Tow Thomas Biquadratic filter etc. The parameters measured are the variations in node voltages & DC supply current. These parameters are specifically chosen for extracting the data, because of their ability to improve the efficiency of ANN.
References
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