In semiconductor industry, CD-SEMs (Critical Dimension Scanning Electron Microscopes) play a pivotal role in metrology and process control. Furthermore, additional applications harnessing the potential of CD-SEM equipment have been developed, including contour extraction algorithms, to address the increasing metrological demands. The task of detecting deformity in semiconductor processes has become a formidable challenge due to the continuous reduction in device feature sizes as we progress from one node to another. Deformity in this case can be described as simulation extra prints or detected manufacturing defects. In this paper, we have utilized vector calculations to detect deformity and calculate roughness. Our product, Calibre ContourCal, facilitates the computation of differences between lithographical simulations and measurements. By evaluating the Root Mean Square Error (RMSE) value, we can identify defects in the SEM image or unexpected extra prints in simulation. Additionally, we have proficiently positioned the sites in critical areas based on contour curvature. To evaluate the quality of the model’s simulated contours, we have introduced a novel indicator based on the difference between the simulated model's Power Spectral Density (PSD) and the fitted PSD. The computation of RMSE for a large FoV (Field of View) containing high site count, exceeding one million sites for example, is a demanding task. For that we parallelized our calculation method using GPUs. With the utilization of GPUs, we have achieved significant time savings, simplifying the processing of high site count. Consequently, we can efficiently detect deformity and calculate roughness for large FoV.
BackgroundFor complex two-dimensional (2D) patterns, optical proximity correction (OPC) model calibration flows cannot always satisfy accuracy requirements with the standard cutline-based input data. Utilizing after-development inspection e-beam metrology image contours, better model predictions of 2D shapes and wafer hotspots can be realized.AimWe compare model accuracy performance of conventional cutline-based and contour-based OPC models on the regular and hotspots patterns.ApproachBy utilizing image contours that are directly extracted from large field of view (LFoV) e-beam metrology, OPC models were calibrated and verified with both cutline-based and contour-based modeling flows. We also used a wafer sampling plan that contained bridging hotspots. Using that sampling plan, a hotspot-aware three-dimentional resist (R3D) compact model was created.ResultsFirst, a contour-based OPC model was generated with <1 nm root mean square error of contour sites. Compared with cutline-based models, it shows better predictions on 2D feature corners. Second, when combined with a hotspot sampling plan, a hotspot-aware compact model could be generated. The accuracy of hotspot predictions on false positives and false negatives was reduced to around 1% with this approach.ConclusionsOPC model calibration and verification with LFoV image contours provide improved predictions on corner rounding shapes and great potential to increase design space coverage. We also observed improved accuracy of hotspot predictions when using an update hotspot aware model when comparing with that of the OPC model. Furthermore, the combination of R3D and stochastic compact models also demonstrated great potential on predictions of rare wafer failure events.
KEYWORDS: Optical proximity correction, Electron beam lithography, 3D modeling, Inspection, Calibration, Lithography, Data modeling, Time metrology, Semiconducting wafers, Scanning electron microscopy
A method to perform Optical Proximity Correction (OPC) model calibration that is also sensitive to lithography failure modes and takes advantage of the large field of view (LFoV) e-beam inspection, is presented. To improve the coverage of the OPC model and the accuracy of the after development inspection (ADI) pattern hotspots prediction - such as trench pinching or bridging in complex 2D routing patterns - a new sampling plan with additional hotpot locations and the corresponding contours input data is introduced. The preliminary inspected hotspots can be added to the traditional OPC modeling flow in order to provide extra information for a hotspot aware OPC model. A compact optical/resist 3D modeling toolkit is applied to interpret the impact of photoresist (PR) profiles, as well as accurate predictions of hotspot patterns occurring at the top or bottom of the PR. A contour-based modeling flow is also introduced that uses a site or edge based calibration engine, to better describe hotspot locations in the hotspot aware OPC model calibration. To quantify the improvement in pattern coverage in the modeling flow, feature vectors (FVs) analysis and comparisons between the conventional and the hotspot aware OPC models is also presented.[1] The time and cost of using conventional Critical Dimension Scanning Electron Microscope (CD-SEM) metrology to measure such a large amount of CD gauges are prohibitive. By contrast, using LFoV e-beam inspection with improved training algorithm to extract fine contours from wafer hotspots, a hotspot aware OPC model can predict ADI hotspots with a higher capture rate as compared to main feature OPC model. Presumably, a hotspot-aware modeling flow based on LFoV images/contours not only benefits users by improving the capture rate of the lithography defects, but also brings the advantages to the failure mode analysis for the post-etch stage.
The method to perform Optical Proximity Correction (OPC) model calibration with contour-based input data from both small field of view (SFoV) and large field of view (LFoV) e-beam inspection is presented. For advanced OPC models - such as Neural Network Assisted Models (NNAM) [1], pattern sampling is a critical topic, where pattern feature vectors utilized in model training, such as image parameter space (IPS) is critical to ensure accurate model prediction [2-5]. In order to improve the design space coverage, thousands of gauges with unique feature vector combinations might be brought into OPC model calibration to improve pattern coverage. The time and cost in conventional Critical Dimension Scanning Electron Microscope (CD-SEM) metrology to measure this large amount of CD gauges is costly. Hence, an OPC modeling solution with contourbased input has been introduced [6]. Built on this methodology, a single inspection image and SEM contour can include a large amount of information along polygon edges in complex logic circuit layouts. Namely, a better feature vector coverage could be expected [7]. Furthermore, much less metrology time is needed to collect the OPC modeling data comparing to conventional CD measurements. It is also shown that by utilizing large field 2D contours, which are difficult to characterize by CD measurements, in model calibration the model prediction of 2D features is improved. Finally, the model error rms of conventional SFoV modeling and LFoV contour modeling between SEM contours and simulation results are compared.
Optical Proximity Correction (OPC) becomes complicated, shrinking a design rule. As a result, measurement points have
increased, and improving the OPC model quality has become more difficult. To improve OPC simulation cost,
Contour-based OPC-modeling is superior to CD-based, because Contour-based shape based rich information. Hence,
Contour-based OPC-modeling is imperative in the next generation lithography, as reported in SPIE2010[5].
In this study, Mask SEM-contours were input into OPC model calibration in order to verify the impact of mask pattern
shape on the quality of the OPC model. Advanced SEM contouring technology was applied to both of Wafer CD-SEM
and Mask CD-SEM in examining the effectiveness of OPC model calibration. The evaluation results of the model quality
will be reported. The advantage of Contour based OPC modeling using Wafer SEM-Contour and Mask SEM-Contour in
the next generation computational lithography will be discussed.
As design rules shrink, Optical Proximity Correction (OPC) becomes complicated. As a result, measurement points have
increased, and improving the OPC model quality has become more difficult. From the viewpoint of decreasing OPC
calibration runtime and improving OPC model quality concurrently, Contour-based OPC-modeling is superior to
CD-based OPC-modeling, because Contour-based OPC-modeling uses shape based rich information. Hence,
Contour-based OPC-modeling is imperative in the next generation lithography, as reported in SPIE2010.
In this study, Mask SEM-contours were input into OPC model calibration in order to verify the impact of mask pattern
shape on the quality of the OPC model. Advanced SEM contouring technology was applied to both of Wafer CD-SEM
and Mask CD-SEM in examining the effectiveness of OPC model calibration. The evaluation results of the model quality
will be reported. The advantage of Contour based OPC modeling using Wafer SEM-Contour and Mask SEM-Contour in
the next generation computational lithography will be discussed.
Optical proximity correction (OPC) modeling is traditionally based on critical dimension (CD) measurements. As design rules shrink and process windows become smaller, there is an unavoidable increase in the complexity of OPC resolution enhancement technique (RET) schemes required to enable design printability. The number of measurement points for OPC modeling has increased to several hundred points per layer, and metrology requirements are no longer limited to simple 1-D measurements. Contour-based OPC modeling has recently arisen as an alternative to the conventional CD-based method. In this work, the technology of contour alignment and averaging is extended to arbitrary 2-D structures. Furthermore, the quality of scanning electron microscope (SEM) contours is significantly improved in cases where the image has both horizontal and vertical edges (as is the case for most 2-D structures) by a new SEM image method, which we call fine SEM edge (FSE). OPC model calibration is done using SEM contours from 2-D structures. Then, the effectiveness of contour-based calibration is examined by doing model verification. The experimental results of the model quality with innovative SEM contours that was developed by Hitachi High-Technologies Corporation (Ibaraki-ken, Japan) are reported. This combination of advanced alignment and averaging, and FSE technologies, makes the best use of the advantage of contour-based OPC-modeling, and should be of use for next-generation lithography.
OPC-modeling is traditionally based on CD-measurements. As design rules shrink, and process window become smaller,
there is an unavoidable increase in the complexity of OPC/RET schemes required to enable design printability. The
number of measurement points for OPC-modeling has increased to several hundred points per layer, and metrology
requirements are no longer limited to simple one-dimensional measurements. Contour-based OPC-modeling has recently
arisen as an alternative to the conventional CD-based method.
In this paper, the technology of contour alignment and averaging was extended to arbitrary 2D structures. Furthermore
the quality of SEM-contours was significantly improved in cases where the image has both horizontal and vertical edges
(as is the case for most 2D structures), by a new SEM image method, which we call 'Fine SEM Edge'. OPC model
calibration was done using SEM-contours from 2D structures. Then, the effectiveness of Contour-based calibration was
examined by doing OPC model verification. The experimental results of the model quality with innovative
SEM-contours with Fine SEM Edge (FSE) and Advanced alignment and averaging that was developed by Hitachi
High-Technologies are reported. This combination of advanced alignment and averaging and FSE technologies makes the best use of the advantage of the contour-based OPC-modeling, and should be of use for the next generation lithography.
SEM contours are used to complement CD measurements in OPC model calibration. This is done to capture 2D
information about printed features into the model while CD measurement data is kept to maintain accuracy for 1D
features. As the method progresses, there are emerging challenges that are normally not found in CD based calibration.
One such challenge is the need to align SEM contours with calibration features. This is particularly important in
determining model accuracy since contour calibration typically involves a cost function that compares the SEM contours
to the simulated print images.
This work explores a technique to include contour alignment errors into the calibration cost function. For each contour
and its corresponding simulated print, the cost function returns an error value for a given set of model parameters. The
error represents how well the model simulation compared to input contour. In addition, it also contains information on
how far or how close the contour is aligned to simulation. Misalignment is to be eliminated on the fly during calibration
and to be reported at the end of calibration. In this paper we describe the proposed technique and compare the results of
calibration between aligned and misaligned contour data.
Site-based SEM measurements produce accurate OPC models in 180nm to 65nm technology nodes, but the lack of 2D
information has prompted for new calibration methods for sub 65nm designs. A hybrid technique using site-based SEM
measurements together with SEM contours has been developed to produce more accurate OPC models. Contour samples
account for 2D effects while CD sites provide high accuracy 1D measurements. SEM contours are prone to sampling
and processing errors as well as extensive calibration run time. We develop a method to filter out inferior samples prior
to model calibration to effectively decrease calibration runtime and increase model accuracy. Fitness and coverage
metrics are used to assess the quality of the contour data in order to select the best subset of the calibration contours.
Our results demonstrate a selection routine that consistently performs better than picking contours at random, and we
discuss the trade-offs between coverage, accuracy and runtime with respect to model quality.
We developed a new contouring technology that executes contour re-alignment based on a matching of the measured
contour with the design data. By this 'secondary' pattern matching (the 'primary' being the pattern recognitions that is
done by the SEM during the measurement itself), rotation errors and XY shifts are eliminated, placing the measured
contour at the correct position in the design coordinates system. In the next phase, the developed method can generate
an averaged contour from multiple SEM images of identical structures, or from plural contours that are aligned
accurately by the algorithm we developed.
When the developed contouring technology is compared with the conventional one, it minimizes contouring errors and
pattern roughness effects to the minimum and enables contouring that represents the contour across the wafer.
The Contour that represents the contour across the wafer we call "Measurement Based Averaged Contour" or MBAC.
We will show that an OPC model that is built from these MBACs is more robust than an OPC model built from
contours that did not get this additional re-alignment.
Traditionally OPC models are calibrated to match CD measurements from selected test pattern locations. This demand
for massive CD data drives advances in metrology. Considerable progress has recently been achieved in complimenting
this CD data with SEM contours. Here we propose solutions to some challenges that emerge in calibrating OPC models
from the experimental contours. We discuss and state the minimization objective as a measure of the distance between
simulation and experimental contours. The main challenge is to correctly process inevitable gaps, discontinuities and
roughness of the SEM contours. We discuss standardizing the data interchange formats and procedures between OPC
and metrology vendors.
KEYWORDS: Photomasks, Calibration, Data modeling, Process modeling, Optical proximity correction, Scanning electron microscopy, Semiconducting wafers, Electron beam lithography, Lithography, Reticles
With the push toward the 32nm node, OPC modeling must respond in kind with additional accuracy enhancements.
One area of lithographic modeling that has basically gone unchecked is mask fidelity. Mask linearity is typically built
into the OPC model since the calibration data contain this information, but mask pattern fidelity is almost impossible to
quantify for OPC modeling. Mask fidelity is the rounding and smoothing of the mask features relative to the post-OPC
layout intent, and there is no robust metric available to quantify these effects. With the introduction of contour-based
model calibration, mask fidelity modeling is possible. This work evaluates techniques to quantify mask modeling and
methods to gauge the accuracy improvement that mask fidelity modeling would project into the lithographic process
using contour-based mask model calibration.
Process models are responsible for the prediction of the latent image in the resist in a lithographic process. In order for
the process model to calculate the latent image, information about the aerial image at each layout fragment is evaluated
first and then some aerial image characteristics are extracted. These parameters are passed to the process models to
calculate wafer latent image. The process model will return a threshold value that indicates the position of the latent
image inside the resist, the accuracy of this value will depend on the calibration data that were used to build the process
model in the first place.
The calibration structures used in building the models are usually gathered in a single layout file called the test pattern.
Real raw data from the lithographic process are measured and attached to its corresponding structure in the test pattern,
this data is then applied to the calibration flow of the models.
In this paper we present an approach to automatically detect patterns that are found in real designs and have
considerable aerial image parameters differences with the nearest test pattern structure, and repair the test patterns to
include these structures. This detect-and-repair approach will guarantee accurate prediction of different layout fragments
and therefore correct OPC behavior.
In order to achieve the necessary OPC model accuracy, the requisite number of SEM CD measurements has
exploded with each technology generation. At 65 nm and below, the need for OPC and/or manufacturing
verification models for several process conditions (focus, exposure) further multiplies the number of
measurements required. SEM-contour based OPC model calibration has arisen as a powerful approach to
deliver robust and accurate OPC models since every pixel now adds information for input into the model,
substantially increasing the parameter space coverage. To date however, SEM contours have been used to
supplement the hundreds or thousands of discreet CD measurements to deliver robust and accurate models.
While this is still perhaps the optimum path for high accuracy, there are some cases where OPC test
patterns are not available, and the use of existing circuit patterns is desirable to create an OPC model.
In this work, SEM contours of in-circuit patterns are utilized as the sole data source for OPC model
calibration. The use scenario involves 130 nm technology which was initially qualified for production with
the use of rule-based OPC, but is shown to benefit from model based OPC. In such a case, sub-nanometer
accuracy is not required, and in-circuit features can enable rapid development of sufficiently accurate
models to provide improved process margin in manufacturing.
Lithography models for leading-edge OPC and design verification must be calibrated with empirical data, and this data is traditionally collected as a one-dimensional quantification of the features acquired by a CD-SEM. Two-dimensional proximity features such as line-end, bar-to-bar, or bar-to-line are only partially characterized because of the difficulty in transferring the complete information of a SEM image into the OPC model building process. A new method of two-dimensional measurement uses the contouring of large numbers of SEM images acquired within the context of a design based metrology system to drive improvement in the quality of the final calibrated model.
Hitachi High-Technologies has continued to develop "full automated EPE measurement and contouring function" based on design layout and detected edges of SEM image. This function can measure edge placement error everywhere in a SEM image and pass the result as a design layout (GDSII) into Mentor Graphics model calibration flow. Classification of the critical design elements using tagging scripts is used to weight the critical contours in the evaluation of model fitness.
During process of placement of the detected SEM edges of into the coordinate system of the design, coordinate errors inevitably are introduced because of pattern matching errors. Also, line edge roughness in 2D features introduces noise that is large compared to the model building accuracy requirements of advanced technology nodes. This required the development of contour averaging algorithms. Contours from multiple SEM images are acquired of a feature and averaged before passing into the model calibration. This function has been incorporated into the prototype Calibre Workbench model calibration flow.
Based on these methods, experimental data is presented detailing the model accuracy of a 45nm immersion lithography process using traditional 1D calibration only, and a hybrid model calibration using SEM image contours and 1D measurement results. Error sources in the contouring are assessed and reported on including systematic and random variation in the contouring results.
Conventional site-base model calibration approaches have worked fine from the 180nm down to the 65nm technology nodes, but with the first 45nm technology nodes rapidly approaching, site-based model calibration techniques may not capture the details contained in these 2D-intensive designs. Due to the compaction of designs, we have slowly progressed from 1D-intensive gates, which were site-based friendly, to very complex and sometimes ornate 2D-gate regions. To compound the problem, these 2D-intensive gate regions are difficult to measure resulting in metrology-induced error when attempting to add these regions to the model calibration data. To achieve the sub-nanometer model accuracy required at this node, a model calibration technique must be able to capture the curvature induced by the process and the design in these gate regions. A new approach in model calibration had been developed in which images from a scanning electron microscope (SEM) are used together with the conventional site-base to calibrate models instead of the traditional single critical dimension (CD) approach. The advantage with the SEM-image model calibration technique is that every pixel in the SEM image contributes as CD information improving model robustness. Now the ornate gate regions could be utilized as calibration features allowing the acquisition of fine curvature in the design.
This paper documents the issues of the site-base model calibration technique at the 45nm technology node and beyond. It also demonstrates the improvement in model accuracy for critical gate regions over the traditional modeling technique, and it shows the best know methods to achieve the utmost accuracy. Lastly, this paper shows how SEM-based modeling quantifies modeling error in these complex 2D regions.
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