PROCEEDINGS ARTICLE | March 19, 2015
Proc. SPIE. 9424, Metrology, Inspection, and Process Control for Microlithography XXIX
KEYWORDS: Target detection, Semiconductors, Optical filters, Metrology, Detection and tracking algorithms, Calibration, Inspection, Scanning electron microscopy, Precision measurement, Overlay metrology
In advanced semiconductor industries, the overlay error budget is getting tighter due to shrinkage in technology. To
fulfill the tighter overlay requirements, gaining every nanometer of improved overlay is very important in order to
accelerate yield in high-volume manufacturing (HVM) fabs. To meet the stringent overlay requirements and to overcome
other unforeseen situations, it is becoming critical to eliminate the smallest imperfections in the metrology targets used
for overlay metrology. For standard cases, the overlay metrology recipe is selected based on total measurement
uncertainty (TMU). However, under certain circumstances, inaccuracy due to target imperfections can become the
dominant contributor to the metrology uncertainty and cannot be detected and quantified by the standard TMU. For
optical-based overlay (OBO) metrology targets, mark asymmetry is a common issue which can cause measurement
inaccuracy, and it is not captured by standard TMU.
In this paper, a new calibration method, Archer Self-Calibration (ASC), has been established successfully in HVM fabs
to improve overlay accuracy on image-based overlay (IBO) metrology targets. Additionally, a new color selection
methodology has been developed for the overlay metrology recipe as part of this calibration method. In this study,
Qmerit-calibrated data has been used for run-to-run control loop at multiple devices. This study shows that color filter
can be chosen more precisely with the help of Qmerit data. Overlay stability improved by 10~20% with best color
selection, without causing any negative impact to the products. Residual error, as well as overlay mean plus 3-sigma,
showed an improvement of up to 20% when Qmerit-calibrated data was used. A 30% improvement was seen in certain
electrical data associated with tested process layers.
