Journal of Electronic Testing: Theory and Applications
Analysing company performance using templates
Intelligent Data Analysis
Analog fault detection using RBF neural networks
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
Use of artificial intelligence techniques to fault diagnosis in analog systems
ECC'08 Proceedings of the 2nd conference on European computing conference
Analog fault detection and classification using genetic algorithm
CIS'09 Proceedings of the international conference on Computational and information science 2009
An intelligent BIST mechanism for MEMS fault detection
CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
Initialized RHPNN for fault detection in MEMS
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
A new probabilistic neural network for fault detection in MEMS
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Analog fault detection using a neuro fuzzy pattern recognition method
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques such as neural networks have been employed to automate classification. The major drawback to such techniques has been the implicit assumption that the variances of the responses of faulty circuits have been the same as each other and the same as that of the fault-free circuit. This assumption can be shown to be false. Neural networks, moreover, have proved to be slow. This paper describes a new neural network structure that clusters responses assuming different means and variances. Sophisticated statistical techniques are employed to handle situations where the variance tends to zero, such as happens with a fault that causes a response to be stuck at a supply rail. Two example circuits are used to show that this technique is significantly more accurate than other classification methods. A set of responses can be classified in the order of 1 s