Recently pattern overlay accuracy becomes more important because of the small pitch patterning. Immersion technology
enabled usage of hyper NA beyond 1.0 and this technology provided a lot of possibility to make a very small patterns.
But there was no significant technical jump for overlay. Therefore chip makers started to compensate non-linear
systematic overlay errors. For example, high order inter-field overlay correction is used to improve overlay performance
between the tool to tool matching. Now chip makers are planning to compensate in-shot(intra-field) overlay with higher
order compensation than before. Scanner vendors provide intra-field matching options such as i-HOPC(intra-field high
order process correction - ASML) and SDM (Super Distortion Matching - Nikon). Those are the methods to match inshot
overlay easily. However there are a lot of arguments what the correct way is to measure the in-shot overlay and how
we can feedback those measured data to APC system. Especially for the distortion measurement of scanner, we have
different data from the mass production trend of distortion.
The pattern dependency and another cause of in-shot (intra-field) overlay error will be defined. This will provide a clue
to solve difference between the mass production in-shot overlay trend and machine distortion data. The final goal of this
study is providing a small hint to design APC system controlling the in-shot(intra-field) overlay with less overlay error.
As the device's design rule has been scaled down, the needs of robust and accurate OPC (Optical Proximity Correction)
model have been increased. In order to meet the needs, we adopt the method of increasing the image parameter space
coverage, such as using SEM-contour based OPC model which provides hundreds or thousands of measurement data set
from each SEM image. It differs from traditional model calibration measurement data set from 1D or 2D symmetric test
pattern which is just one CD measurement data from one pattern.
In SEM contour-based model OPC, it is important that what kinds of patterns are chosen for model calibration and how
the SEM image contours are extracted to improve model accuracy.
In this paper, we selected the SEM images for SEM contour modeling analyzing aerial image intensity variation. As
finding optically sensitive patterns, we could make robust and accurate OPC model across the process window. In this
SEM-contour based OPC modeling, we applied the method from commercial SEM company.
With the use of 193nm lithography, time-dependent haze problem has become a critical issue for semiconductor industry. The understanding of the conditions that create haze defects is very crucial for the future development of haze-free cleaning processes. The gaseous environment trapped between the pellicle film and the mask surface triggers photochemical reaction under laser exposure, which could result in the formation of killer (printable) defects on the mask surface. Therefore, the real time analysis of the haze environment in the trapped space could provide essential clues to the characterization of haze defect growth mechanism. This fundamental study can be applied to the invention of real-time monitoring tools for the defect growth progress on the mask surface as well as the development of haze-free cleaning processes. Here, we propose a method to analyze the gaseous space trapped between the pellicle film and the mask surface that creates a highly reactive environment.