Generating decision regions in analog measurement spaces

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  B. Hardware
  B.7 INTEGRATED CIRCUITS
      B.7.3 Reliability and Testing**

General Terms:
Algorithms, Reliability

Keywords:
analog circuits, implicit functional test, neural networks