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.
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, and 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 (Presented at SPIE Advanced Lithography: March 01, 2018; Published: 13 March 2018); https://doi.org/10.1117/12.2297182.
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