Image paradigm for semiconductor defect data reduction

Kenneth W. Tobin, Shaun S. Gleason, Thomas P. Karnowski, Hamed Sari-Sarraf, Marylyn H. Bennett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Automation tools for semiconductor defect data analysis are becoming necessary as device density and wafer sizes continue to increase. These tools are needed to efficiently and robustly process the increasing amounts of data to quickly characterize manufacturing processes and accelerate yield learning. An image-based method is presented for analyzing process 'signatures' from defect data distributions. This paper describes the statistical and morphological image processing methods used to achieve an automated segmentation of signature events into high-level process-oriented categories. Applications are presented for enhanced statistical process control, automatic process characterization, and intelligent subsampling of event distributions for off-line, high-resolution defect review.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsSusan K. Jones
Pages194-205
Number of pages12
StatePublished - 1996
EventMetrology, Inspection, and Process Control for Microlithography X - Santa Clara, CA, USA
Duration: Mar 11 1996Mar 13 1996

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2725

Conference

ConferenceMetrology, Inspection, and Process Control for Microlithography X
CitySanta Clara, CA, USA
Period03/11/9603/13/96

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