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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 [1][4][5].
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 [2].
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%.
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