Ensembles of clustering algorithms for problem of detection of homogeneous production batches of semiconductor devices
Rozhnov, I. P.
Orlov, V. I.
Kazakovtsev, L. A.
Институт управления бизнес-процессами и экономики
Кафедра экономики и информационных технологий менеджмента
Journal Name:CEUR Workshop Proceedings
Journal Quartile in Scopus:без квартиля
Journal Quartile in Web of Science:без квартиля
Bibliographic Citation:Rozhnov, I. P. Ensembles of clustering algorithms for problem of detection of homogeneous production batches of semiconductor devices [Текст] / I. P. Rozhnov, V. I. Orlov, L. A. Kazakovtsev // CEUR Workshop Proceedings. — 2018. — Т. 2098. — С. 338-348
To complete the on-board equipment of space systems with a highly reliable electronic component base (ECB), specialized test centers perform hundreds of tests to analyze each semiconductor device. One of the requirements is that the shipped lot of products must be made from a single batch of raw materials (wafers) which is not guaranteed if the devices are not manufactured for use in the space industry only. To solve the problem of detecting homogeneous production batches, various clustering algorithms are implemented on multidimensional data of test results. In practice, it is impossible to predict in advance which of the algorithms in each particular case will show the most adequate results and the use of the ensemble approach is promising. Most of the clustering algorithms for the problem of dividing the ECB mixed lot into two homogeneous production batches show rather high accuracy. With an increase in the number of homogeneous production batches in the mixed lot, the accuracy decreases. Authors propose an approach to constructing an ensemble of clustering algorithms based on co-occurrence matrices with weight coefficients. Results of computational experiments on specially mixed lots of the ECB show that for the such large-scale problems, the use of the ensemble approach allows to achieve a higher adequacy of the results. Individual algorithms can show results that exceed the ensemble's accuracy, but the accuracy of the ensemble is still higher than the averaged accuracy of individual algorithms.