14 April 2014 Run time scanner data analysis for HVM lithography process monitoring and stability control
Author Affiliations +
Proceedings Volume 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII; 90502J (2014); doi: 10.1117/12.2046330
Event: SPIE Advanced Lithography, 2014, San Jose, California, United States
Abstract
There are various data mining and analysis tools in use by high-volume semiconductor manufacturers throughout the industry that seek to provide robust monitoring and analysis capabilities for the purpose of maintaining a stable lithography process. These tools exist in both online and offline formats and draw upon data from various sources for monitoring and analysis. This paper explores several possible use cases of run-time scanner data to provide advanced overlay and imaging analysis for scanner lithography signatures. Here we demonstrate several use-cases for analyzing and stabilizing lithography processes. Applications include high order wafer alignment simulations in which an optimal alignment strategy is determined by dynamic wafer selection, reporting statistics data and analysis of the lot report and the sub-recipe as a sort of non-standard lot report, visualization of key lithography process parameters, and scanner fleet management (SFM)
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Woong Jae Chung, Young Ki Kim, John Tristan, Jeong Soo Kim, Lokesh Subramany, Chen Li, Brent Riggs, Vidya Ramanathan, Ram Karur-Shanmugam, George Hoo, Jie Gao, Anna Golotsvan, Kevin Huang, Bill Pierson, John Robinson, "Run time scanner data analysis for HVM lithography process monitoring and stability control", Proc. SPIE 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII, 90502J (14 April 2014); doi: 10.1117/12.2046330; https://doi.org/10.1117/12.2046330
PROCEEDINGS
8 PAGES


SHARE
KEYWORDS
Optical alignment

Scanners

Semiconducting wafers

Lithography

Visualization

Data analysis

Statistical analysis

Back to Top