Presentation + Paper
28 March 2018 Defect detection strategies and process partitioning for SE EUV patterning
Author Affiliations +
Abstract
The key challenge for enablement of a 2nd node of single-expose EUV patterning is understanding and mitigating the patterning-related defects that narrow the process window. Typical in-line inspection techniques, such as broadband plasma (291x) and e-beam systems, find it difficult to detect the main yield-detracting defects post-develop, and thus understanding the effects of process improvement strategies has become more challenging. New techniques and methodologies for detection of EUV lithography defects, along with judicious process partitioning, are required to develop process solutions that improve yield.

This paper will first discuss alternative techniques and methodologies for detection of lithography-related defects, such as scumming and microbridging. These strategies will then be used to gain a better understanding of the effects of material property changes, process partitioning, and hardware improvements, ultimately correlating them directly with electrical yield detractors .
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luciana Meli, Karen Petrillo, Anuja De Silva, John Arnold, Nelson Felix, Chris Robinson, Benjamin Briggs, Shravan Matham, Yann Mignot, Jeffrey Shearer, Bassem Hamieh, Koichi Hontake, Lior Huli, Corey Lemley, Dave Hetzer, Eric Liu, Ko Akiteru, Shinichiro Kawakami, Takeshi Shimoaoki, Yusaku Hashimoto, Hiroshi Ichinomiya, Akiko Kai, Koichiro Tanaka, Ankit Jain, Heungsoo Choi, Barry Saville, and Chet Lenox "Defect detection strategies and process partitioning for SE EUV patterning", Proc. SPIE 10583, Extreme Ultraviolet (EUV) Lithography IX, 105830E (28 March 2018); https://doi.org/10.1117/12.2297362
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Inspection

Semiconducting wafers

Etching

Stochastic processes

Extreme ultraviolet

Modulation

Defect detection

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