Traditional scanner matching methods have been based in 1D proximity matching targets
and the use of wafer-based CD metrology to characterize both the initial mismatch as
well as the sensitivity of CDs to scanner tuning knobs.
One such method is implemented in ASML Pattern Matcher, which performs a linear
optimization based on user provided CD sensitivities and pre-match data. The user
provided data usually comes from wafer exposures done at multiple scanner illumination
conditions measured with CD-SEM. In the near future ASML plans to provide the
capability to support YieldStar CD data for Pattern Matcher which will collect CD data
with higher precision and much faster turn-around-time that CD-SEM.
Pattern Matcher has been used successfully in multiple occasions. Results for one such
occasion are shown in Figure 1 which presents the through pitch mismatch behavior of
one ASML XT:1400F with respect to an ASML XT:1400E for a 32nm contact layer.
With shrinking feature sizes and error budgets in OPC models, effective pattern coverage and accurate measurement
become more and more challenging. The goal of pattern selection is to maximize the efficiency of gauges used in model
calibration. By optimizing sample plan for model calibration, we can reduce the metrology requirement and modeling
turn-around time, without sacrificing the model accuracy and stability. With the Tachyon pattern-selection-tool, we seek
to parameterize the patterns, by assessing dominant characteristics of the surroundings of the point of interest. This
allows us to represent each pattern with one vector in a finite-dimensional space, and the entire patterns pool with a set
of vectors. A reduced but representative set of patterns can then be automatically selected from the original full set
sample data, based on certain coverage criteria. In this paper, we prove that the model built with 56% reduced wafer data
could achieve comparable quality as the model built with full set data.
Proximity matching is a common activity in the wafer fabs1,2,3 for purposes such as
process transfer, capacity expansion, improved scanner yield and fab productivity. The
requirements on matching accuracy also become more and more stringent as CD error
budget shrinks with the feature size as technology advances. Various studies have been
carried out, using scanner knobs including NA, inner sigma, outer sigma, stage tilt,
ellipticity, and dose. In this paper, we present matching results for critical features of a
logic device, between an ASML XT:19x0i scanner and an XT:1700i (reference),
demonstrating the advantage of freeform illuminator pupil as part of the adjustable
knobs to provide additional flexibility. We also present the investigation of a novel
method using lens manipulators for proximity matching, effectively injecting scalar
wavefront to an XT:19x0i to mimic the behavior of the XT:1700i lens.
FlexRay programmable illumination and LithoTuner software is combined in several use cases. The first use case is
optical proximity error (OPE) minimization. Simulation predicts the rms OPE error is reduced by 39% with LithoTuner
and FlexRay, and is confirmed via experiment with a reduction of 33%. For minimizing the OPE error, two types of
illumination tuning was performed, sigma tuning and freeform tuning. The sigma tuning is able to reduce the mean-totarget
critical dimension (CD) error, but the CD error variance is unaffected. Freeform tuning, however, is able to reduce
both the mean-to-target CD and the CD error variance. The second use case is matching two ArF scanners, a XT:1950Hi
with FlexRay to a XT:1700Fi with diffractive optical element (DOE) illumination. With LithoTuner and FlexRay,
simulation predicts the CD error post-matching is reduced by 51%, and experiment was able to achieve a reduction of
IC manufacturers have a strong demand for transferring a working process from one scanner to another. Recently, a
programmable illuminator (FlexRayTM) became available on ASML ArF immersion scanners that, besides all the
parameterized source shapes of the earlier AerialTM illuminator (based on diffractive optical elements) can also produce
any desired freeform source shape. As a consequence, a fabrication environment may have scanners with each of the
illuminator types so both FlexRay-to-Aerial and FlexRay-to-FlexRay matching is of interest. Moreover, the FlexRay
illuminator itself is interesting from a matching point-of-view, as numerous degrees of freedom are added to the
matching tuning space.
This paper demonstrates how the upgrade of an exposure tool from Aerial to FlexRay illuminator shows identical
proximity behavior without any need for scanner tuning. Also, an assessment of the imaging correspondence between
exposure tools each equipped with a FlexRay illuminator is made. Finally, for a series of use-cases where proximity
differences do exist, the application of FlexRay source tuning is demonstrated. It shows an enhancement of the scanner
matching capabilities, because FlexRay source tuning enables matching where traditional NA and sigma tuning are
shortcoming. Moreover, it enables tuning of freeform sources where sigma tuning is not relevant. Pattern MatcherTM
software of ASML Brion is demonstrated for the calculation of the optimized FlexRay tuned sources.
Scanner matching based on wafer data has proven to be successful in the past years, but its adoption into production has
been hampered by the significant time and cost overhead involved in obtaining large amounts of statistically precise
wafer CD data. In this work, we explore the possibility of optical model based scanner matching that maximizes the use
of scanner metrology and design data and minimizes the reliance on wafer CD metrology.
A case study was conducted to match an ASML ArF immersion scanner to an ArF dry scanner for a 6Xnm technology
node. We used the traditional, resist model based matching method calibrated with extensive wafer CD measurements
and derived a baseline scanner manipulator adjustment recipe. We then compared this baseline scanner-matching recipe
to two other recipes that were obtained from the new, optical model based matching method. In the following sections,
we describe the implementation of both methods, provide their predicted and actual improvements after matching, and
compare the ratio of performance to the workload of the methods. The paper concludes with a set of recommendations
on the relative merits of each method for a variety of use cases.
Given the continually decreasing k1 factor and process latitude in advanced technology nodes, it is important to fully
understand and control the variables that impact imaging behavior in the lithography process. In this joint work between
TSMC and ASML, we use model-based simulations to characterize and predict the imaging effects of these variables
and to fine-tune the scanner settings based on such information in order to achieve optimal printing results on a perreticle
basis. The scanner modeling makes use of detailed scanner characteristics as well as wafer CD measurements for
accurate model construction. Simulations based on the calibrated model are subsequently used to predict the wafer
impact of changes in tunable scanner parameters for all critical patterns in the product. The critical patterns can be
identified beforehand, either experimentally on wafer, mask or through model simulations. A set of optimized scanner
setting offsets, known as a "scanner tuning recipe" is generated to improve the imaging behavior for the critical patterns.
We have demonstrated the efficacy of this methodology for multiple-use cases with selected ASML scanners and TSMC
processes and will share the achieved improvements on defect reduction and yield improvements.
Given the decrease in k1 factor for 65nm-node lithography technology and beyond, it is increasingly important to
understand and control the variables which impact scanner imaging behavior in the lithography process. In this work, we
explore using model simulations to characterize and predict imaging effects of these variables, and then based on such
information to fine-tune the scanner settings to obtain printing results optimally matched to a reference scanner. The
scanner modeling makes use of detailed scanner characteristics as well as wafer CD measurements for accurate model
construction. To identify critically mismatched patterns on a production layout, we employ the fast full-chip simulation
capability provided by Brion's Tachyon servers. Tachyon simulations are also used to predict wafer impacts of changes
in tunable scanner parameters. A set of optimized scanner variable offsets, called a "scanner tuning recipe", is generated
to minimize overall imaging mismatch between two scanners. As a proof-of-concept, we have carried out scanner tuning
procedures on selected ASML scanners. The results show improvements more than 20% on CD offset RMS values for
2D line-end patterns, production layout patterns, and the mismatched patterns identified with the full-chip simulation.
Improvements on wafer-acceptance-test results and production yield on the to-be-tuned scanner are also observed.
The challenge for the upcoming full-chip CD uniformity (CDU) control at 32nm and 22nm nodes is unprecedented with
expected specifications never before attempted in semiconductor manufacturing. To achieve these requirements, OPC
models not only must be accurate for full-chip process window characterization for fine-tuning and matching of the
existing processes and exposure tools, but also be trust-worthy and predictive to enable processes to be developed in
advance of next-generation photomasks, exposure tools, and resists. This new OPC requirement extends beyond the
intended application scope for behavior-lumped models. Instead, separable OPC models are better suited, such that each
model stage represents the physics and chemistry more completely in order to maintain reliable prediction accuracy. The
resist, imaging tool, and mask models must each stand independently, allowing existing resist and mask models to be
combined with new optics models based on exposure settings other than the one calibrated previously.
In this paper, we assess multiple sets of experimental data that demonstrate the ability of the TachyonTM FEM (focus and
exposure modeling) to separate the modeling of mask, optics, and resists. We examine the predictability improvements
of using 3D mask models to replace thin mask model and the use of measured illumination source versus top-hat types.
Our experimental wafer printing results show that OPC models calibrated in FEM to one optical setting can be
extrapolated to different optical settings, with prediction accuracy commensurate with the calibration accuracy. We see
up to 45% improvement with the measured illumination source, and up to 30% improvement with 3D mask.
Additionally, we observe evidence of thin mask resist models that are compensating for 3D mask effect in our wafer data
by as much as 60%.
To minimize or eliminate lithography errors associated with optical proximity correction, integrated circuit manufacturers need an accurate, predictive, full-chip lithography model which can account for the entire process window (PW). We have validated the predictive power of a novel focus-exposure modeling methodology with wafer data collected across the process window at multiple customer sites. Tachyon Focus-Exposure Modeling (FEM) first-principle, physics-driven simulations deliver accurate and predictive full-chip lithography modeling for producing state-of-the-art circuits.