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The mask inspection and defect classification is a crucial part of mask preparation technology and consumes a significant
amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly
precise mask inspection system with high detection sensitivity becomes greater. However, due to the high sensitivity, in
addition to the detection of smaller defects on finer geometries, the inspection machine could report large number of
false defects. The total number of defects becomes significantly high and the manual classification of these defects,
where the operator should review each of the defects and classify them, may take huge amount of time. Apart from false
defects, many of the very small real defects may not print on the wafer and user needs to spend time on classifying them
as well. Also, sometimes, manual classification done by different operators may not be consistent. So, need for an
automatic, consistent and fast classification tool becomes more acute in more advanced nodes.
Automatic Defect Classification tool (NxADC) which is in advanced stage of development as part of NxDAT1, can
automatically classify defects accurately and consistently in very less amount of time, compared to a human operator.
Amongst the prospective defects as detected by the Mask Inspection System, NxADC identifies several types of false
defects such as false defects due to registration error, false defects due to problems with CCD, noise, etc. It is also able to
automatically classify real defects such as, pin-dot, pin-hole, clear extension, multiple-edges opaque, missing chrome,
chrome-over-MoSi, etc.
We faced a large set of algorithmic challenges during the course of the development of our NxADC tool. These include
selecting the appropriate image alignment algorithm to detect registration errors (especially when there are sub-pixel
registration errors or misalignment in repetitive patterns such as line space), differentiating noise from very small real
defects, registering grey level defect images with layout data base, automatically finding out maximum critical
dimension (CD) variation for defective patterns (where patterns could have Manhattan as well as all angle edges), etc.
This paper discusses about many such key issues and suggests strategies to address some of them based upon our
experience while developing the NxADC and evaluating it on production mask defects.
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