One of the main challenges in photomask cleaning is balancing particle removal efficiency (PRE) with pattern
damage control. To overcome this challenge, a high frequency megasonic cleaning strategy is implemented.
Apart from megasonic frequency and power, photomask surface conditioning also influences cleaning
performance. With improved wettability, cleanliness is enhanced while pattern damage risk is simultaneously
reduced. Therefore, a particle removal process based on higher megasonic frequencies, combined with proper
surface pre-treatment, provides improved cleanliness without the unintended side effects of pattern damage, thus
supporting the extension of megasonic cleaning technology into 10nm half pitch (hp) device node and beyond.
A wafer's printed CD error can be impacted by unaccounted mask making process variation. Unaccounted mask CD
and/or corner rounding alters the intended drawn mask pattern contributing to a wafer's printed CD error. During OPC
wafer calibration, average mask bias and corner rounding are accounted for in the OPC model, but random local mask
making process variations or mask-to-mask variations can be difficult to account in such model calibration. Thus when
a wafer's CD has error, it can be difficult to determine if the general root cause was due to mask or wafer or both. An
in-line monitoring application has been developed to extract accurate mask CD and rendered mask polygon from
collected mask CD-SEM images. Technical information will be presented on the challenges of accurately extracting
information from SEM images. In particular, discussions include SEM image calibration, contour extraction, inverse
pattern rendering, and general image processing to account for mask SEM aberrations (translation, rotation, & dilation),
tool-to-tool variation, vendor-to-vendor variation, run-to-run variation, and dark/bright field pattern-to-pattern variation.
After accurate mask SEM contours are obtained, lithographic simulations are performed on extracted polygon contours
to determine the impact of mask variation on wafer CD. This paper will present detail information about the Inverse
Pattern Rendering (IPR) capabilities developed for a virtual Wafer CD (WCD) application and its results, which is
proven to achieved 0.5 nm accuracy across multiple critical layers from 28 nm to 40 nm nodes on multiple CD-SEM
tools over multiple mask shop locations.
As the lithography design rule of IC manufacturing continues to migrate toward more advanced technology nodes, the mask error enhancement factor (MEEF) increases and necessitates the use of aggressive OPC features. These aggressive OPC features pose challenges to reticle inspection due to high false detection, which is time-consuming for defect classification and impacts the throughput of mask manufacturing. Moreover, higher MEEF leads to stricter mask defect capture criteria so that new generation reticle inspection tool is equipped with better detection capability. Hence, mask process induced defects, which were once undetectable, are now detected and results in the increase of total defect count. Therefore, how to review and characterize reticle defects efficiently is becoming more significant.
A new defect review system called ReviewSmart has been developed based on the concept of defect grouping disposition. The review system intelligently bins repeating or similar defects into defect groups and thus allows operators to review massive defects more efficiently. Compared to the conventional defect review method, ReviewSmart not only reduces defect classification time and human judgment error, but also eliminates desensitization that is formerly inevitable. In this study, we attempt to explore the most efficient use of ReviewSmart by evaluating various defect binning conditions. The optimal binning conditions are obtained and have been verified for fidelity qualification through inspection reports (IRs) of production masks. The experiment results help to achieve the best defect classification efficiency when using ReviewSmart in the mask manufacturing and development.
As design rule continues to shrink towards ITRS roadmap requirements, reticle defect capture criteria are becoming ever more challenging. Pattern fidelity and reticle defects that were once perceived as insignificant or nuisance are now becoming a significant considerable yield impacting factor. More defects are also detectable and presented with increase in implementation of new generation reticle inspection systems. Therefore, how to review and characterize defects accurately and efficiently is becoming more significant. In particular, defect classification time often corresponds directly to the cost and the cycle time of mask manufacturing or new technology development.
In this study we introduce a new mask defect review tool called ReviewSmart, which retrieves and processes defect images reported from KLA-Tencor's high sensitivity TeraScan inspection tool. Compared to the traditional defect review method, ReviewSmart provides a much better method to manage defects efficiently by utilizing the concept of defect grouping disposition.
Through the application and qualification results with respectable reticle production cases, the implementation of ReviewSmart has been proven to be effective for reducing defect classification loading and improving defect characterizing efficiency. Moreover, the new review tool is helpful to categorically identify tool or process variations thus allowing users to expedite the learning process for developing production worthy leading node processes.