Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Yada Zhu, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. In particular, following the same recipe for a certain IC device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., Each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this paper, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. To the best of our knowledge, we are the first to address this problem. Extensive experiments on benchmark and manufacturing data sets demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1121-1126
Number of pages6
Volume2015-January
EditionJanuary
DOIs
Publication statusPublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

Other

Other14th IEEE International Conference on Data Mining, ICDM 2014
CountryChina
CityShenzhen
Period12/14/1412/17/14

    Fingerprint

Keywords

  • co-clustering
  • structural
  • temporal

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Zhu, Y., & He, J. (2015). Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing. In Proceedings - IEEE International Conference on Data Mining, ICDM (January ed., Vol. 2015-January, pp. 1121-1126). [7023457] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2014.17