A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Automatic Detection of Spatial Signature on Wafermaps in a High Volume Production
DFT '99 Proceedings of the 14th International Symposium on Defect and Fault-Tolerance in VLSI Systems
Process Diagnosis via Electrical-Wafer-Sorting Maps Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A neural-network approach for an automatic LED inspection system
Expert Systems with Applications: An International Journal
ACID: Automatic Sort-Map Classification for Interactive Process Diagnosis
IEEE Design & Test
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Expert Systems with Applications: An International Journal
Separation of composite defect patterns on wafer bin map using support vector clustering
Expert Systems with Applications: An International Journal
The predictions of optoelectronic attributes of LED by neural network
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Detection and classification of defect patterns in optical inspection using support vector machines
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Journal of Electronic Testing: Theory and Applications
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In semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In the literature, Kohonen's Self Organizing Feature Maps (SOM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the real data sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOM as wafer classifiers is re-assessed on much more extensive simulated and real data sets. We conclude that ART1 is not adequate, whereas SOM provide completely satisfactory results including visually effective representation of spatial failure probability of the pattern classes.