13 March 2018 Computational metrology: enabling full-lot high-density fingerprint information without adding wafer metrology budget, and driving improved monitoring and process control
Author Affiliations +
Abstract
Computational metrology has been proposed as the way forward to resolve the need for increased metrology density, resulting from extending correction capabilities, without adding actual metrology budget. By exploiting TWINSCAN based metrology information, dense overlay fingerprints for every wafer can be computed. This extended metrology dataset enables new use cases, such as monitoring and control based on fingerprints for every wafer of the lot. This paper gives a detailed description, discusses the accuracy of the fingerprints computed, and will show results obtained in a DRAM HVM manufacturing environment. Also an outlook for improvements and extensions will be shared.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hyun-Sok Kim, Min-Sung Hyun, Jae-Wuk Ju, Young-Sik Kim, Cees Lambregts, Peter van Rhee, Johan Kim, Elliott McNamara, Wim Tel, Paul Böcker, Nang-Lyeom Oh, Jun-Hyung Lee, "Computational metrology: enabling full-lot high-density fingerprint information without adding wafer metrology budget, and driving improved monitoring and process control", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851P (13 March 2018); doi: 10.1117/12.2297182; https://doi.org/10.1117/12.2297182
PROCEEDINGS
11 PAGES + PRESENTATION

SHARE
Back to Top