caa a22 4500 475833929 CHVBK 20180406123848.0 cr unu---uuuuu 170329e20001001xx s 000 0 eng 10.1007/s005210070013 doi (NATIONALLICENCE)springer-10.1007/s005210070013 Error Classification and Yield Prediction of Chips in Semiconductor Industry Applications [Elektronische Daten] [L. Ludwig, E. Sapozhnikova, V. Lunin, W. Rosenstiel] 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.: Springer-Verlag London Limited, 2000 Keywords: Adaptive Resonance Theory nationallicence Neural networks nationallicence Process Control Monitoring nationallicence Quality testing nationallicence Ludwig L. Department for Computer Engineering, University of Tuebingen, Tuebingen, Germany, DE aut Sapozhnikova E. Department for Electrical Engineering and Introscopy, Moscow Power Engineering Institute (Technical University), Moscow, Russia, RU aut Lunin V. Department for Electrical Engineering and Introscopy, Moscow Power Engineering Institute (Technical University), Moscow, Russia, RU aut Rosenstiel W. Department for Computer Engineering, University of Tuebingen, Tuebingen, Germany, DE aut https://doi.org/10.1007/s005210070013 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s005210070013 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ludwig L. Department for Computer Engineering, University of Tuebingen, Tuebingen, Germany, DE aut NATIONALLICENCE 700 1- Sapozhnikova E. Department for Electrical Engineering and Introscopy, Moscow Power Engineering Institute (Technical University), Moscow, Russia, RU aut NATIONALLICENCE 700 1- Lunin V. Department for Electrical Engineering and Introscopy, Moscow Power Engineering Institute (Technical University), Moscow, Russia, RU aut NATIONALLICENCE 700 1- Rosenstiel W. Department for Computer Engineering, University of Tuebingen, Tuebingen, Germany, DE aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer