Detection of homogeneous production batches of semiconductor devices by greedy heuristic clustering algorithms with special distance metrics

Authors present a comparative efficiency analysis of application of k-means and k-medoids clustering models for solving the problem of grouping of semiconductor devices into homogeneous production batches using three types of metrics: Euclidean distance, Mahalanobis distance, Manhattan distance.

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-01, Vol.734 (1), p.12104
Main Authors: Shkaberina, G Sh, Rozhnov, I P, Popov, V P, Kazakovtsev, L A, Lapunova, E V
Format: Article
Language:English
Subjects:
Clustering
Euclidean geometry
Greedy algorithms
Semiconductor devices
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Description
Summary:Authors present a comparative efficiency analysis of application of k-means and k-medoids clustering models for solving the problem of grouping of semiconductor devices into homogeneous production batches using three types of metrics: Euclidean distance, Mahalanobis distance, Manhattan distance.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/734/1/012104