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171
Voted
GLVLSI
2005
IEEE
2005
IEEE
Generating decision regions in analog measurement spaces
We develop a neural network that learns to separate the nominal from the faulty instances of a circuit in a measurement space. We demonstrate that the required separation boundaries are, in general, non-linear. Unlike previous solutions which draw hyperplanes, our network is capable of drawing the necessary non-linear hypersurfaces. The hypersurfaces translate to test criteria that are strongly correlated to functional tests. A feature selection algorithm interacts with the network to identify a discriminative lowdimensional measurement space. Categories and Subject Descriptors B.7.3 [Integrated Circuits]: Reliability and Testing General Terms Algorithms, Reliability Keywords Analog Circuits, Neural Networks, Implicit Functional Test
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| Added | 24 Jun 2010 |
| Updated | 24 Jun 2010 |
| Type | Conference |
| Year | 2005 |
| Where | GLVLSI |
| Authors | Haralampos-G. D. Stratigopoulos, Yiorgos Makris |
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