Error classification and yield prediction of chips in semiconductor industry applications

In this paper, we present the application of supervised neural algorithms b ased on Adaptive Resonance Theory (Fuzzy ARTMAP, ART-EMAP and distributed A RTMAP), as well as some feedforward networks (counter-propagation, backprop agation, Radial Basis Function algorithm) to the quality, testing problem i n the semiconductor industry. The aim is to recognise and classify, deviati ons in the results of functional and Process-Control-Monitoring (PCM) tests of chips as soon as they are available so that technological corrections c all be implemented more quickly. This goal can be divided in two tasks that are treated in this paper: the classification of faulty wafers on the basi s of topological information extracted front functional tests; and forecast ing the yield of chips using the results of PCM tests. Experiments show tha t the neural networks can be applied to this problem efficiently, and the p erformance of ART algorithms is better than that of the other architectures .