Conference Paper

Unsupervised algorithms for the automatic classification of EWS maps: a comparison

Pavia Univ., Italy
DOI: 10.1109/ISSM.2005.1513349 Conference: Semiconductor Manufacturing, 2005. ISSM 2005, IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT Recently, it has been shown that the classification of electrical wafer sorting failure maps can be performed by means of unsupervised methods. In this work four different unsupervised methods are compared: SOM, K-means, neural gas, and an expectation maximization. The algorithms are compared using a benchmark based on a probabilistic model. The performance of the classification is assessed by means of an new index, called index-F, based on the knowledge of the real classification. Moreover it is studied the correlation between the proposed index and the following indexes: CH-index, D-index, I-index and average likelihood.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The commonality analysis is a proven tool for fault detection in semiconductor manufacturing. This methodology extracts subsets of production lots from all the available data. Then, data mining techniques are used only on the selected data. This approach loses part of the available information and does not discriminate among the lots. The new methodology performance the automatic classification of the electrical wafer test maps in order to identify the classes of failure present in the production lots. Subsequently, the proposed procedure uses the process history of each wafer to create a list of the root cause candidates. This methodology is the core of the software tool ACID which is currently used for process diagnosis at the Agrate site of the ST Microelectronics. A real analysis is presented.
    Data Mining, Fifth IEEE International Conference on; 12/2005


1 Download
Available from