The introduction of EUV lithography into the semiconductor fabrication process will enable a continuation
of Moore's law below the 22nm technology node. EUV lithography will, however, introduce new sources
of patterning distortions which must be accurately modeled and corrected with software. Flare caused by
scattered light in the projection optics result in pattern density-dependent imaging errors. The combination
of non-telecentric reflective optics with reflective reticles results in mask shadowing effects. Reticle
absorber materials are likely to have non-zero reflectivity due to a need to balance absorber stack height
with minimization of mask shadowing effects. Depending upon placement of adjacent fields on the wafer,
reflectivity along their border can result in inter-field imaging effects near the edge of neighboring
exposure fields. Finally, there exists the ever-present optical proximity effects caused by diffractionlimited
imaging and resist and etch process effects. To enable EUV lithography in production, it is
expected that OPC will be called-upon to compensate for most of these effects. With the anticipated small
imaging error budgets at sub-22nm nodes it is highly likely that only full model-based OPC solutions will
have the required accuracy. The authors will explore the current capabilities of model-based OPC software
to model and correct for each of the EUV imaging effects. Modeling, simulation, and correction
methodologies will be defined, and experimental results of a full model-based OPC flow for EUV
lithography will be presented.
The introduction of EUV lithography into the semiconductor fabrication process will enable a continuation
of Moore's law below the 22 nm technology node. EUV lithography will, however, introduce new and
unwanted sources of patterning distortions which must be accurately modeled and corrected on the
reticle. Flare caused by scattered light in the projection optics is expected to result in several nanometers of
on-wafer dimensional variation, if left uncorrected. Previous work by the authors has focused on
combinations of model-based and rules-based approaches to modeling and correction of flare in EUV
lithography. Current work to be presented here focuses on the development of an all model-based approach
to compensation of both flare and proximity effects in EUV lithography. The advantages of such an
approach in terms of both model and OPC accuracy will be discussed. In addition, the authors will discuss
the benefits and tradeoffs associated with hybrid OPC approaches which mix both rules-based and modelbased
OPC. The tradeoffs to be explored include correction time, accuracy, and data volume.
The critical role of flare in extreme ultraviolet (EUV) lithography is well known. In this work, the implementation of a robust flare metrology is discussed, and the proposed approach is qualified both in terms of precision and accuracy. The flare measurements are compared to full-chip simulations using a simplified single fractal point-spread function (PSF), and the parameters of the analytical PSF are optimized by comparing the simulation output to the experimental results. After flare map calibration, the matching of simulation and experiment in the flare range from 4 to 12% is quite good, clearly indicating an offset of about 3%. The origin of this offset is attributed to the presence of DUV light. An experimental estimate of the DUV component is found in good agreement with the predicted value.
The 22nm node will be patterned with very challenging Resolution Enhancement Techniques (RETs) such
as double exposure or double patterning. Even with those extreme RETs, the k1 factor is expected to be
less than 0.3. There is some concern in the industry that traditional edge-based simulate-then-move Optical
Proximity Correction (OPC) may not be up to the challenges expected at the 22nm node. Previous work
presented the advantages of a so-called inverse OPC approach when coupled with extreme RETs or
illumination schemes. The smooth mask contours resulting from inverse corrections were shown not to be
limited by topological identity, feedback locality, or fragment conformity. In short, inverse OPC can
produce practically unconstrained and often non-intuitive mask shapes. The authors will expand this
comparison between traditional and inverse OPC to include likely 22nm RETs such as double dipole
lithography and double patterning, comparing dimensional control through process window for each OPC
method. The impact of mask simplification of the inverse OPC shapes into shapes which can be reliably
manufactured will also be explored.
As more aggressive Resolution Enhancement Techniques (RET) are applied, the problem of correctly
fragmenting edges of an OPC mask is becoming more complex. OPC recipes contain more lines devoted to
control of fragmentation than anything else. This paper introduces a new Automatic Adaptive Fragmentation
method that decreases the complexity of OPC recipes while providing the same or better quality of results. The
adaptive fragmentation is guided by just a few simple rules that provide flexible fragmentation while adhering to
mask manufacturing rule constraints.
The process model is a major factor affecting the quality of the Model Based Optical Proximity Correction
(OPC). Better process model directly leads to better OPC, hence better yield and more profit. While the
traditional way in calibrating these process models is using CD measurements at sample locations in the test chip,
however, the use of Scanning Electron Microscope (SEM) image contours for process model calibration and
optimization has been recently introduced in trial to build more predictable models. In this study, we characterize
the traditional flow models versus the contour calibrated models and study the effect of using different
combinations and weighting schemes on the quality of the resulting process models, its stability and its ability to
correctly predict the process.
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