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

• Type: Conference
• Year: 2005
• Where: GLVLSI
• Authors: Haralampos-G. D. Stratigopoulos, Yiorgos Makris
• Keywords: Functional Test, GLVLSI 2005, Measurement Space, Neural Networks, VLSI