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Pattern Analysis and Applications

, Volume 17, Issue 4, pp 863–881 | Cite as

Evaluating the reliability level of virtual metrology results for flexible process control: a novelty detection-based approach

  • Pilsung Kang
  • Dongil Kim
  • Sungzoon Cho
Industrial and Commercial Application

Abstract

The purpose of virtual metrology (VM) in semiconductor manufacturing is to support process monitoring and quality control by predicting the metrological values of every wafer without an actual metrology process, based on process sensor data collected during the operation. Most VM-based quality control schemes assume that the VM predictions are always accurate, which in fact may not be true due to some unexpected variations that can occur during the process. In this paper, therefore, we propose a means of evaluating the reliability level of VM prediction results based on novelty detection techniques, which would allow flexible utilization of the VM results. Our models generate a high-reliability score for a wafer’s VM prediction only when its process sensor values are found to be consistent with those of the majority of wafers that are used in model building; otherwise, a low-reliability score is returned. Thus, process engineers can selectively utilize VM results based on their reliability level. Experimental results show that our reliability generation models are effective; the VM results for wafers with a high level of reliability were found to be much more accurate than those with a low level.

Keywords

Virtual metrology Reliability level Novelty detection Semiconductor Process monitoring 

Notes

Acknowledgments

The first author was supported by the research program funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2011-0021893) and by the Ministry of Science, ICT, and Future Planning (NRF-2014R1A1A1004648). The dataset used in this paper can be available upon request.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.School of Industrial Management EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Department of Industrial EngineeringSeoul National UniversitySeoulSouth Korea

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