Reduction in on-product overlay random error using machine learning algorithm

Song, Gil-Woo; Kim, Sungchai; Park, Sung Won; Jo, Taeyong; Kum, Euiseok; Hwang, Jong-Hyun; Lee, Seonho

doi:10.1117/12.2612771

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those strategies is implemented aided by machine learning for APC, or auto process correction, by automatically learning the smoothing factor over the wafer area. [5] However, it's hard to maintain robustness and consistency of all of the channels for many layers so requirement for manageability limit the number of channels available. Moreover, the characteristics of a swing curve [6] constrain the number of channels for specific layer.…”

Section: Simulation Of Apocwmentioning

confidence: 99%

Exploiting spatial characteristic of wafer for effective OCW: area partitioning OCW

Song

Kang

Kim

et al. 2023

Metrology, Inspection, and Process Control XXXVII

View full text Add to dashboard Cite

Optimal color weighting (OCW) is a promising technique to improve accuracy and robustness of alignment mark measurement in the lithography process. Measurement based OCW shows remarkable improvement of overlay error under laboratory conditions. In those conditions, one of the wafer processes is split and a simple form of mark deformation is present. However, the OCW effect has not been confirmed in the case of on-product overlay since various of deformations are mixed in the real FAB. We perform simple simulation showing that mixed deformations can deteriorate the performance of OCW and show that utilizing spatial characteristics of the wafer with OCW further improves the overlay error. As a result, we have improved on-product-overlay form 3.78 nm to 3.51 nm or 7.1% using data of 86 lots, 268 wafers in DUV layer of 3 nm logic device.

show abstract

Section: Simulation Of Apocwmentioning

confidence: 99%