Patterned masks require requalification at wafer fabrication plants. Periodic inspections are done at the wafer fab to identify any new defects, such as haze and contamination, which develop or get added on the mask due to their usage and the way they are handled. These defects, if not monitored over time, may result in mask defects that print on the wafer. It is thus mandatory to identify and fix them in their early stages. Repeated inspections, coupled with higher sensitivity inspections employed at wafer fabs, result in a large number of defects reported, which includes many small and faint defects. However, some of these small and faint defects need detailed operator review time, due to their potential to grow and have a larger impact later. Efficiency of classification includes both speed and accuracy. For a manual review, accuracy is primarily affected by consistency, arising due to the bulk and monotonous nature of classification task affected by human fatigue. The requirement for higher accuracy however, necessitates increased operator review and analysis time. Increased operator review time translates to the amount of time a mask in not used for printing wafers, i.e. productivity loss. Calibre® DefectClassify™ tool enables automatic classification of defects by employing stable algorithms to ensure consistency and accuracy, while algorithm efficiency ensures adequate speed. The tool thus aids in improving the throughput and yield at wafer fabs. The tool reads defect images, analyzes image properties to extract potential defect regions, processes the regions to identify actual defects and classifies them. This paper mainly focuses on the challenges faced in characterization and classification of defects from images reported by the inspection machine. The primary difference between analyzing inspections at wafer fab and mask shop is the availability of layout data. Unavailability of layout data complicates the tasks of identifying different pattern regions on the mask, especially assist features. With advanced technology nodes, the number of assist features present is higher while the features themselves get smaller in size. These features, if not identified correctly, may be mistaken as defects. Other than that, single die defects are a category that gets affected due to lack of layout information. Without a reference to compare with, these defects require separate sets of rules to be applied to images for their identification and classification. In particular, identification of defects on pattern edges and corners from single die images is challenging without a reference image.
EUV Defect avoidance techniques will play a vital role in extreme ultraviolet lithography (EUVL) photomask fabrication with the anticipation that defect free mask blanks won’t be available and that cost effective techniques will not be available for defect repairing. In addition, mask shops may not have a large inventory of expensive EUV mask blanks. Given these facts, defect avoidance can be used as cost effective technique to optimize the mask blank and design data (mask data) pair selection across mask blank manufacturers and mask shops so that overall mask blank utilization can be enhanced.
In previous work, it was determined that the pattern shift based solution increases the chance that a defective mask blank can be used that would otherwise be discarded . In pattern shift, design data is shifted such that defects are either moved to isolated regions or hidden under the patterns that are written. However pattern shifts techniques don’t perform well with masks with higher defect counts. Pattern shift techniques in this form assume all defects to be equally critical. In addition, a defect is critical or important only if it lands on the main pattern. A defect landing on fill, sub-resolution assist feature (SRAF) or fiducial areas may not be critical. In this paper we assess the performance of pattern shift techniques assuming defects that are not critical based upon size or type, as well as defects landing in non-critical areas (smart shift) can be ignored.
In a production mask manufacturing environment it is necessary to co-optimize and prioritize blank-design pairing for multiple mask layouts in the queue with the available blanks. A blank-design pairing tool maximizes the utilization of blanks by finding the best pairing between blanks and design data so that the maximum number of mask blanks can be used. In this paper we also propose a novel process which would optimize the usage of costly EUV mask blanks across mask blank manufacturers and mask shops which write masks.
At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it’s imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts printability of defects at wafer level and automates the process of defect dispositioning from images captured using high resolution inspection machine. It first eliminates false defects due to registration, focus errors, image capture errors and random noise caused during inspection. For the remaining real defects, actual mask-like contours are generated using the Calibre® ILT solution , which is enhanced to predict the actual mask contours from high resolution defect images. It enables accurate prediction of defect contours, which is not possible from images captured using inspection machine because some information is already lost due to optical effects. Calibre’s simulation engine is used to generate images at wafer level using scanner optical conditions and mask-like contours as input. The tool then analyses simulated images and predicts defect printability. It automatically calculates maximum CD variation and decides which defects are severe to affect patterns on wafer. In this paper, we assess the printability of defects for the mask of advanced technology nodes. In particular, we will compare the recovered mask contours with contours extracted from SEM image of the mask and compare simulation results with AIMSTM for a variety of defects and patterns. The results of printability assessment and the accuracy of comparison are presented in this paper. We also suggest how this method can be extended to predict printability of defects identified on EUV photomasks.
The blank mask defect review process involves detailed analysis of defects observed across a substrate’s multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies.
Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs.
Calibre® MDPAutoClassify™ provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and report significant defect information such as defect size, affecting defect printability and mask usability. The algorithm’s capabilities are challenged by the variety and complexity of defects encountered, in terms of defect nature, size, shape and composition; and the optical phenomena occurring around the defect .
This paper mainly focuses on the results from the evaluation of Calibre® MDPAutoClassify™ product. The main objective of this evaluation is to assess the capability of accurately estimating the size of the defect from the inspection images automatically. The sensitivity to weak defect signals, filtering out noise to identify the defect signals and locating the defect in the images are key success factors. The performance of the tool is assessed on programmable defect masks and production masks from HVM production flow. Implementation of Calibre® MDPAutoClassify™ is projected to improve the accuracy of defect size as compared to what is reported by inspection machine, which is very critical for production, and the classification of defects will aid in arriving at appropriate dispositions like SEM review, repair and scrap.
A blank mask and its preparation stages, such as cleaning or resist coating, play an important role in the eventual yield obtained by using it. Blank mask defects’ impact analysis directly depends on the amount of available information such as the number of defects observed, their accurate locations and sizes. Mask usability qualification at the start of the preparation process, is crudely based on number of defects. Similarly, defect information such as size is sought to estimate eventual defect printability on the wafer. Tracking of defect characteristics, specifically size and shape, across multiple stages, can further be indicative of process related information such as cleaning or coating process efficiencies. At the first level, inspection machines address the requirement of defect characterization by detecting and reporting relevant defect information. The analysis of this information though is still largely a manual process. With advancing technology nodes and reducing half-pitch sizes, a large number of defects are observed; and the detailed knowledge associated, make manual defect review process an arduous task, in addition to adding sensitivity to human errors. Cases where defect information reported by inspection machine is not sufficient, mask shops rely on other tools. Use of CDSEM tools is one such option. However, these additional steps translate into increased costs. Calibre NxDAT based MDPAutoClassify tool provides an automated software alternative to the manual defect review process. Working on defect images generated by inspection machines, the tool extracts and reports additional information such as defect location, useful for defect avoidance; defect size, useful in estimating defect printability; and, defect nature e.g. particle, scratch, resist void, etc., useful for process monitoring. The tool makes use of smart and elaborate post-processing algorithms to achieve this. Their elaborateness is a consequence of the variety and complexity of defects encountered. The variety arises due to factors such as defect nature, size, shape and composition; and the optical phenomena occurring around the defect. This paper focuses on preliminary characterization results, in terms of classification and size estimation, obtained by Calibre MDPAutoClassify tool on a variety of mask blank defects. It primarily highlights the challenges faced in achieving the results with reference to the variety of defects observed on blank mask substrates and the underlying complexities which make accurate defect size measurement an important and challenging task.
Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for
considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated
substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and
accurate identification of blank substrate defects and the risk they pose to the patterned reticle.
While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of
defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of
adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic
classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of
blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to
select appropriate job-decks to be written on the mask by defect avoidance tools .
Smart algorithms separate critical defects from the potentially large number of non-critical defects or false
defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and
characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected
review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses
a decision tree to translate this information into a defect classification code. Using this automated process improves
classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the
alternative of manual defect classification by trained personnel .
This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP
Mask Technology Center (MPMask). The Calibre ADC tool was qualified on production mask blanks against the manual
classification. The classification accuracy of ADC is greater than 95% for critical defects with an overall accuracy of
90%. The sensitivity to weak defect signals and locating the defect in the images is a challenge we are resolving. The
performance of the tool has been demonstrated on multiple mask types and is ready for deployment in full volume mask
manufacturing production flow. Implementation of Calibre ADC is estimated to reduce the misclassification of critical
defects by 60-80%.
Shrinking feature sizes and the need for tighter CD (Critical Dimension) control require the introduction of new
technologies in mask making processes. One of those methods is the dose assignment of individual shots on VSB
(Variable Shaped Beam) mask writers to compensate CD non-linearity effects and improve dose edge slope. Using
increased dose levels only for most critical features, generally only for the smallest CDs on a mask, the change in mask
write time is minimal while the increase in image quality can be significant. However, this technology requires accurate
modeling of the mask effects, especially the CD/dose dependencies. This paper describes a mask model calibration flow
for Mask Process Correction (MPC) applications with shot dose assignment.
The first step in the calibration flow is the selection of appropriate test structures. For this work, a combination of linespace
patterns as well as a series of contact patterns are used for calibration. Features sizes vary from 34 nm up to
several micrometers in order to capture a wide range of CDs and pattern densities. After mask measurements are
completed the results are carefully analyzed and measurements very close to the process window limitation and outliers
are removed from the data set.
One key finding in this study is that by including patterns exposed at various dose levels the simulated contours of the
calibrated model very well match the SEM contours even if the calibration was based entirely on gauge based CD
values. In the calibration example shown in this paper, only 1D line and space measurements as well as 1D contact
measurements are used for calibration. However, those measurements include patterns exposed at dose levels between
75% and 150% of the nominal dose. The best model achieved in this study uses 2 e-beam kernels and 4 kernels for the
simulation of development and etch effects. The model error RMS on a large range of CD down to 34 nm line CD is
The calibrated model is then used to generate 2D contours for line ends, space ends and contacts and those contours are
compared to SEM images. For all patterns, including those very close to the resolution limit, very good contour overlay
is achieved. It appears that by including the various dose levels in the calibration a very good separation of the e-beam
model components from the etch components is possible and that this also results in very accurate 2D model quality.
In conclusion, very accurate mask model calibration is achieved for mask processes using shot dose assignment.
Standard test patterns can be used for calibration if they include the dose variations intended for correction.
Standard OPC models consist of a physical optical model and an empirical resist model. The resist model compensates the optical model imprecision on top of modeling resist development. The optical model imprecision may result from mask topography effects and real mask information including mask ebeam writing and mask process contributions. For advanced technology nodes, significant progress has been made to model mask topography to improve optical model accuracy. However, mask information is difficult to decorrelate from standard OPC model. Our goal is to establish an accurate mask model through a dedicated calibration exercise. In this paper, we present a flow to calibrate an accurate mask enabling its implementation. The study covers the different effects that should be embedded in the mask model as well as the experiment required to model them.