Autoassociative neural networks for fault diagnosis in semiconductor manufacturing
As yield and productivity are increasingly competing in importance with technology in integrated circuit manufacturing, semiconductor industry can benefit from advances on artificial intelligence. This paper shows a fault diagnosis system based on autoassociative neural networks, a little exploited processing architecture in industrial applications. The system integrates three autoassociative algorithms and it selects the most suitable in each case. It optimizes the processing time while guarantees an accurate diagnosis. The feasibility of the solution is justified and comparative results are presented and discussed.
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