Title
Application of Fast Laser Deprocessing Techniques on large cross-sectional view area sample with FIB-SEM dual beam system.
Abstract
Cross-sectional analysis is one of the important areas for physical failure analysis. Focus Ion Beam (FIB) and mechanical polish sample preparation are commonly used and necessary techniques in the semiconductor industry and Failure Analysis (FA) Company (Wills and Perungulam, 2007). However, each technique has its own limitation. Mechanical polishing technique easily induces artifact by mechanical force, especially on advance technology node. FIB can eliminate mechanically damaged artifact, but have the limitation on cross-sectional view area. Another potential technique will be plasma FIB, it used very high milling current and fast milling speed (Hrnčíř et al., 2013). However, it comes with a very high cost and having the contamination issue. The contamination issue greatly affects the low kV Scanning Electron Microscopy (SEM) imaging quality. In recent semiconductor industry FA, low kV SEM imaging is preferable, because high kV imaging will introduce delamination artifacts especially on organic material from packaged sample. In this paper, Fast Laser Deprocessing Techniques (FLDT) application is further enhanced on large area cross-sectional FA with fast cycle time and low-cost equipment. This is to prevent from mechanical damage. In short, the proposed FLDT is a cost-effective and quick way to deprocess a sample for defect identification in cross-sectional FA.
Year
DOI
Venue
2016
10.1016/j.microrel.2016.07.060
Microelectronics Reliability
Keywords
Field
DocType
Laser deprocessing,FIB,XSEM
Nanotechnology,Polishing,Ion beam,Computer science,Scanning electron microscope,Sample preparation,Laser,Electronic engineering,Beam (structure),Optoelectronics,Semiconductor industry,Delamination
Journal
Volume
ISSN
Citations 
64
0026-2714
0
PageRank 
References 
Authors
0.34
0
15
Name
Order
Citations
PageRank
Y. Z. Zhao101.01
qijie wang200.68
P. K. Tan301.35
H. H. Yap400.68
B. H. Liu500.68
H. Feng600.68
H. Tan701.35
R. He800.34
Yueh-Min Huang92455278.09
D. D. Wang1001.35
L. Zhu1101.01
C. Q. Chen1202.37
Francis Rivai1300.68
J. Lam1401.01
Z. H. Mai1514.34