Haralampos-G. D. Stratigopoulos
ABSTRACT
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 low-dimensional measurement space.
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Digital Library
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Index Terms
- Generating decision regions in analog measurement spaces
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