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SPM (Sulfuric acid peroxide mixture) which has been extensively used for acid cleaning of photomask and wafer has serious drawback for EUV mask cleaning. It shows severe film loss of tantalum-based absorber layers and limited removal efficiency of EUV-generated carbon contaminants on EUV mask surface.
Here, we introduce such novel cleaning chemicals developed for EUV mask as almost film loss free for various layers of the mask and superior carbon removal performance. Combinatorial chemical screening methods allowed us to screen several hundred combinations of various chemistries and additives under several different process conditions of temperature and time, eventually leading to development of the best chemistry selections for EUV mask cleaning.
Recently, there have been many activities for the development of EUV pellicle, driven by ASML and core EUV scanner customer companies. It is still important to obtain film-loss free cleaning chemicals because cleaning cycle of EUV mask should be much faster than that of optic mask mainly due to EUV pellicle lifetime. More frequent cleaning, combined with the adoption of new materials for EUV masks, necessitates that mask manufacturers closely examine the performance change of EUV masks during cleaning process.
We have investigated EUV mask quality changes and film losses during 50 cleaning cycles using new chemicals as well as particle and carbon contaminant removal characteristics. We have observed that the performance of new chemicals developed is superior to current SPM or relevant cleaning chemicals for EUV mask cleaning and EUV mask lifetime elongation.
In order to improve mask quality, it is required to have accurate MPC model which properly describes current mask fabrication process. There are limits on making and defining accurate MPC model because it is hard to know the actual CD trend such as CD linearity and through-pitch owing to the process dispersion and measurement error. To mitigate such noises, we normally measure several sites of each pattern types and then utilize the mean value of each measurement for MPC modeling. Through those procedures, the noise level of mask data will be reduced but it does not always guarantee improvement of model accuracy, even though measurement overhead is increasing. Root mean square (RMS) values which is usually used for accuracy indicator after modeling actually does not give any information on accuracy of MPC model since it is only related with data noise dispersion.
In this paper, we reversely approached to identify the model accuracy. We create the data regarded as actual CD trend and then create scattered data by adding controlled dispersion of denoting the process and measurement error to the data. Then we make MPC model based on the scattered data to examine how much the model is deviated from the actual CD trend, from which model accuracy can be investigated. It is believed that we can come up with appropriate method to define the reliability of MPC model developed for optimized process corrections.
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 [1].
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.
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