Translator Disclaimer
Presentation + Paper
22 February 2021 Stochastic defect criticality prediction enabled by physical stochastic modeling and massive metrology
Author Affiliations +
Abstract
With the adoption of extreme ultraviolet (EUV) lithography for high volume production in the advanced wafer manufacturing fab, defects resulting from stochastic effects could be one of major yield killers and draw increasing interest from the industry. In this paper, we will present a flow, including stochastic edge placement error (SEPE) model calibration, pattern recognition and hot spot ranking from defect probability, to detect potential hot spot in the chip design. The prediction result shows a good match with the wafer inspection. HMI eP5 massive metrology and contour analysis were used to extract wafer statistical edge placement distribution data.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
ChangAn Wang, Peigen Cao, Maxence Delorme, Jen-Yi Wuu, Jiyou Fu, Fuming Wang, Bob Lin, Yiqiong Zhao, Yi-Hsing Peng, Yongfa Fan, Mu Feng, Bin Cheng, Jen-Shiang Wang, Mark Simmoms, Stefan Hunsche, Oliver Patterson, Kuo-Feng Pao, Abdalmohsen Elmalk, Kevin Gao, Ruochong Fei, Xuefeng Zeng, and Xiaolong Zhang "Stochastic defect criticality prediction enabled by physical stochastic modeling and massive metrology", Proc. SPIE 11609, Extreme Ultraviolet (EUV) Lithography XII, 1160916 (22 February 2021); https://doi.org/10.1117/12.2584767
PROCEEDINGS
9 PAGES + PRESENTATION

SHARE
Advertisement
Advertisement
Back to Top