Sub-resolution Assist Features (SRAFs) are powerful tools to enhance the focus margin of drawn patterns. SRAFs are
placed and sized so they do not print on the wafer, but the larger the SRAF, the more effective it becomes at enhancing
through-focus stability. The size and location of an SRAF that will image on a wafer is highly dependent upon
neighboring patterns and models of SRAF printability are, at present, unreliable. Model-based SRAF placement has
been used to enhance resolution at 20nm node processes and below with stringent requirements that inserted SRAFs will
not be imaged on wafer. However, despite widespread SRAF use and hard data as to SRAF effectiveness, it has been
very difficult to develop a process model that accurately predicts under what process conditions an SRAF will image on
a wafer. More accurate models of SRAF printing should allow model based SRAF placement to be relaxed, resulting in
more effective SRAF placement and broader focus margins.
One of the first problems with the concept of SRAF printability is the definition of an SRAF printing on a wafer. This is
not obvious because two different states of printing exist. The first print state is when a residue is left on a wafer from
the SRAF. The first state can be considered printing from the point of view that photoresist is on the wafer and the
photoresist may even lift off and cause defects. However, the first state can be considered non-printing because the over
etch from the etch process will generally remove the photoresist residual and the material underneath. The second state
is when a pattern is formed and etched into the substrate, a state at which the pattern has clearly printed on the wafer. Of
course, intermediate states may also be defined. In order to be applicable, an SRAF printability model must be able to
predict both printing states. In addition, the model must be able to extrapolate to configurations beyond those used to
develop the model in the first place. These model properties may then be used to optimize the printability vs. efficacy of
an SRAF either prior to or during an Optical Proximity Correction (OPC) run.
The process models that are used during OPC have never been able to reliably predict which SRAFs will print. This
appears to be due to the fact that OPC process models are generally created using data that does not include printed subresolution
An enhancement to compact modeling capability to predict Assist Features (AF) printability is developed and discussed.
A hypsometric map representing 3-D resist profile was built by applying a first principle approximation to estimate the
"energy loss" from the resist top to bottom. Such a 3-D resist profile is an extrapolation of a well calibrated traditional
OPC model without any additional information. Assist features are detected at either top of resist (dark field) or bottom
of resist (bright field). Such detection can be done by just extracting top or bottom resist models from our 3-D resist
model. There is no measurement of assist features needed when we build AF but it can be included if interested but
focusing on resist calibration to account for both exposure dosage and focus change sensitivities. This approach
significantly increases resist model's capability for predicting printed SRAF accuracy. And we don't need to calibrate an
SRAF model in addition to the OPC model. Without increase in computation time, this compact model can draw assist
feature contour with real placement and size at any vertical plane. The result is compared and validated with 3-D
rigorous modeling as well as SEM images. Since this method does not change any form of compact modeling, it can be
integrated into current MBAF solutions without any additional work.
An enhancement to compact modeling capability to include photoresist (PR) loss at different heights is
developed and discussed. A hypsometric map representing 3-D resist profile was built by applying a first
principle approximation to estimate the "energy loss" from the resist top to any other plane of interest as a
proportional corresponding change in model threshold, which is analogous to a change in exposure dose. The
result is compared and validated with 3D rigorous modeling as well as SEM images. Without increase in
computation time, this compact model can construct 3D resist profiles capturing resist profile degradation at
any vertical plane. Sidewall angle and standing wave information can also be granted from the vertical profile
reconstruction. Since this method does not change any form of compact modeling, it can be integrated to
validation and correction without any additional work.
The mechanism of chemically amplified resist plays a critical role in the modeling of the latent image. To achieve a
practical model which can fit into the time frame of OPC, some simplifications and assumptions have to be made. We
introduced regression kernels that take into account best exposure focus difference between isotropic pitch, dense, and
line end features for the evaluation of image intensity. It compares the image intensity (signal) over small changes
above and/or below the regressed "nominal" image position, which in principle corresponds to evaluating the intensity
signal at various depths of a fixed resist profile thus can also be regressed for optimization during model development.
Our calibration has shown that the model brought a great improvement in prediction for difficult structures such as dense
features at or near the optical resolution limit and 2-dimensional features, which are the limiter of the overall model
fitting accuracy for 45nm node and below. By replacing other existing techniques, total number of output kernels used
for OPC operation is actually reduced with improvement of model accuracy. This model is proven to be a very effective
yet accurate addition to the current OPC technology.
In passive millimeter wave (PMMW) imaging, antenna size limitations lead to the problem of poor resolution of
acquired image. Thus efficient post-processing is necessary to achieve resolution improvement. In this paper, we present
an Adaptive Porjected Landweber super-resolution algorithm that attempts to leverage the strong points of both
Landweber iteration and projection-based adjustments. In the algorithm, we implement the Landweber iterations as the
main image restoration scheme and include a projection-based adjustment for enforcing constraints after each Landweber
iteration is completed. Furthermore, the algorithm updates the parameter adaptively at each iteration. From experiments,
we find that the Adaptive Projected Landweber superresolution algorithm obtains better results and has lower mean
square error (MSE) and produces sharper images. These constraints and adaptive characters speed up the convergence of
the Landweber algorithm.