As critical dimension shrinks, the image quality of lithographic projection lens is improved gradually. In lithographic tool, vibration is an important factor affecting the image quality of projection lens. The internal vibration of projection lens will change the relative position between the lens elements, reduce the image contrast and process window. There are usually two methods to suppress the internal vibration of lithographic projection lens, isolating the external vibration source and improving the stiffness of the internal structure. Based on the above methods, this paper proposes a dynamic imaging model (DIM) that establishes a precise quantitative relationship between the internal vibration of projection lens and the imaging contrast. The DIM has been used in image performance analysis and the structure design for lithographic projection lens.
In lithographic scanner, many different physical factors could impact to image quality and CD uniformity. In optical
systems, the pupil filling quality (source shape), wavefront error and stray light can decrease the intensity contrast and
shrink the process window. In mechanical domain, the vibration and scanning synchronization error have the similar
effect to imaging process.
Imaging in scanner is a dynamic exposure process and in this process, aerial image should keep the same relative
position to the wafer. It requests the lithographic tool must have a very stable mechanical frame and very good motion
control performance. In addition, the wafer stage, reticle stage's coordinate and projection lens' grid should be matched
exactly, include the scanning direction and velocity ratio. The tool's alignment system can calibrate the statistic
coordinate for overlay, but it cannot calibrate the dynamic coordinate in scanning direction very well because projection
lens' grid has a small asymmetric signiture. This systematic error should be calibrated for CDU improvement.
An imaging model considering the motion blurring is represented in this paper and based on this model, the dynamic
coordinate's error could be analyzed. Furthermore, exposure method can be used to calibrate the dynamic coordinate and
improve the CD uniformity.
Exposure latitude will be used to check and calibrate the lithographic tool's dynamic coordinate. We designed a special
calibration process to obtain the best dynamic coordinate setting for scanner. In this process, some tool's coordinate
parameters (scanning skew and scale) have been changed for every field to obtain the multi-dimensions' exposure
information. Exposure window can be represented from this result, and in this exposure window, the best dynamic
coordinate setting could be found. After the dynamic coordinate calibrated, the CDU is improved.
Lithographic tool performance is the main contributor to CDU. The tool designers and users require an accurate method
to measure the tool's error factors on the wafer side in order to improve CDU. Engineers typically use the FEM method
to estimate DOF and EL, and then predict the CDU. However, based on the exposure data, it is often difficult to separate
systematic level physical errors, such as DOSE repeatability, focus repeatability, dynamic errors and all the other tool's
In this paper, we introduce a wafer data based method to diagnose tool's performance for CDU improvement. As the
systematic errors have a specific signature, they generate a fingerprint in the exposure data. Based on the knowledge of
the exposure process and process flow, multiple dimensions exposure matrix is designed to analyze and diagnose the
tool's systematic error from wafer data fingerprint.
For SMEE's scanner tool (SSA600/10), we use this method to diagnose tool's systematic error and improve the CDU.
Some typical result is represented in this paper.
In this paper, we present a streamlined aerial image model that is linear with respect to projection optic's aberrations. The
model includes the impact of the NA, partial coherence, as well as the aberrations on the full aerial image as measured on
an x-z grid. The model allows for automatic identification of image's primary degrees of freedom, such as bananicity and
Y-icity among others. The model is based on physical simulation and statistical analysis. Through several stages of
multivariate analysis a reduced dimensionality description of image formation is obtained, using principal components
on the image side and lumped factors on the parameter side. The modeling process is applied to the aerial images
produced by the alignment sensor in a 0.75NA ArF scanner while the tool is integration mode and aberration levels are
high. Approximately 20 principal components are found to have a high signal-to-noise ratio in the image set produced
by varying illumination conditions and considering aberrations represented by 33 Zernike polynomials. The combined
coefficients are extracted and the measurement repeatability is presented. The analysis portion of the model is then
applied to the measured coefficients and a subset of projection lens' aberrations are solved for.
In this paper, we present an image quality model and a process window model that is linear or quadratic with respect to
common pupil space errors. Similar to other CDU models in its simplicity, our model expands linear representation to
comprehensive image quality specs in a large focus-dose grid. With this model we identify corrections to the full
Bossung curve or process window shapes that are proportional to aberration levels.