Error Classification and Yield Prediction of Chips in Semiconductor Industry Applications

In this paper, we present the application of supervised neural algorithms based on Adaptive Resonance Theory (Fuzzy ARTMAP, ART-EMAP and distributed ARTMAP), as well as some feedf-orward networks (counter-propagation, backpro-pagation, Radial Basis Function algorithm) to the quality testing problem in the semiconductor industry. The aim is to recognise and classify deviations in the results of functional and Process-Control-Monitoring (PCM) tests of chips as soon as they are available so that technological corrections can 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 basis of topological information extracted from functional tests; and forecasting the yield of chips using the results of PCM tests. Experiments show that the neural networks can be applied to this problem efficiently, and the performance of ART algorithms is better than that of the other architectures.