The use of unpatterned wafer inspection for immersion lithography defectivity studies

被引:2
|
作者
Holsteyns, Frank [1 ]
Cheung, Lisa [2 ]
van den Heuvel, Dieter [1 ]
Marcuccilli, Gino [2 ]
Simpson, Gavin [2 ]
Brun, Roland [2 ]
Steinbach, Andy [2 ]
Fyen, Wim [1 ]
Vangoidsenhoven, Diziana [1 ]
Mertens, Paul [1 ]
Maenhoudt, Mireille [1 ]
机构
[1] IMEC, Kapeldreef 75, B-3001 Heverlee, Belgium
[2] KLA Tencor, Milpitas, CA USA
关键词
immersion lithography; wafer defectivity; inspection; topcoat; 193mn resist; unpatterned defect monitoring; SP1; SP2;
D O I
10.1117/12.656728
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The switch from dry to immersion lithography has important consequences regarding wafer defectivity. It has been shown that for successful and efficient defect reductions related to immersion lithography the capability to distinguish immersion/patterning related defects from stack related defects is very useful during process control. These stack related defects can be observed after careful partitioning of individual layer inspections and the analysis of this data through DSA in Klarity. The optimisation of the dark field inspection SP2 tool, central in this paper, shows that improved sensitivity at adequate signal to noise ratio can be obtained on the resist stacks by using the smaller wavelength as the UV-Iaser light present in the SP2. For bare Si and BARC oblique incidence illumination gives the best sensitivity and captures the most defects. However monitoring of the resist and stacks with resist requires normal incidence illumination since the nature of defects and film result in a higher scattering intensity using normal illumination. The use of an optical filter and a 10% laser power also contributed to establishing a lower and stable background signal for each inspection scan. As immersion tool development is improved and immersion specific defectivity is reduced, the proportion of the stack related defects will become a significant fraction of the overall target for further defect reduction. This includes point defects (embedded particles) or flow defects (streaks) identified and classified using SURFimage. Finally this information is to be used to identify the defect origin(s) for ultimate elimination of defects in the stacks.
引用
收藏
页数:17
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