Floating search methods in feature selection
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Analog and Mixed-Signal Benchmark Circuits-First Release
Proceedings of the IEEE International Test Conference
Analog testing by characteristic observation inference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Prediction of analog performance parameters using fast transient testing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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.