Statistical methods for visual defect metrology

Automated systems are used to inspect unpatterned and product wafers for particulates and other defects. Wafer defect count and defect density statistics are used as process control parameters, but are known to be deceptive in the presence of defect clustering. An improvement path using novel visual...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on semiconductor manufacturing Vol. 11; no. 1; pp. 48 - 53
Main Authors Cunningham, S.P., MacKinnon, S.
Format Journal Article Conference Proceeding
LanguageEnglish
Published New York, NY IEEE 01.02.1998
Institute of Electrical and Electronics Engineers
Subjects
Analysis of variance
Applied sciences
Contamination
Electronics
Exact sciences and technology
Inspection
Metrology
Microelectronic fabrication (materials and surfaces technology)
Monitoring
Optical films
Pattern recognition
Process control
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Statistical analysis
Statistics
Statistical method
Metrology
Microelectronic fabrication
Automatic measurement
Defect detection
Online AccessGet full text

Cover

More Information
Summary:Automated systems are used to inspect unpatterned and product wafers for particulates and other defects. Wafer defect count and defect density statistics are used as process control parameters, but are known to be deceptive in the presence of defect clustering. An improvement path using novel visual defect metrology statistical analyses is proposed. Quadrat analysis, nested analysis of variance, and principal component analysis use data available currently. Spatial point pattern statistics and spatial pattern recognition require special algorithms. Future process control systems made possible by these statistical analyses are discussed.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0894-6507
1558-2345
DOI:10.1109/66.661284