Resolution limitations in the mask making process can cause differences between the features that appear in a database
and those printed to a reticle. These differences may result from intentional or unintentional features in the database
exceeding the resolution limit of the mask making process such as small gaps or lines in the data, line end shortening on
small sub-resolution assist features etc creating challenges to both mask writing and mask inspection. Areas with high
variance from design to mask, often referred to as high MEEF areas (mask error enhancement factor), become highly
problematic and can directly impact mask and device yield, mask manufacturing cycle time and ultimately mask costs.
Specific to mask inspection it may be desirable to inspect certain non-critical or non-relevant features at reduced
sensitivity so as not to detect real, but less significant process defects. In contrast there may also be times where
increased sensitivity is required for critical mask features or areas. Until recently, this process was extremely manual,
creating added time and cost to the mask inspection cycle. Shifting to more intelligent and automated inspection flows is
the key focus of this paper. A novel approach to importing design data directly into the mask inspection to include both
MDP generated MRC errors files and LRC generated MEEF files.
The results of recently developed inspection and review capability based upon controlling defect inspection using design
aware data base control layers on a pixel basis are discussed. Typical mask shop applications and implementations will
be shown.
Design Based Metrology (DBM) implements a novel automation flow, which allows for a direct
and traceable correspondence to be established between selected locations in product designs and
matching metrology locations on silicon wafers. Thus DBM constitutes the fundamental enabler of
Design For Manufacturability (DFM), because of its intrinsic ability to characterize and quantify the
discrepancy between design layout intent and actual patterns on silicon. The evolution of the CDSEM
into a DFM tool, capable of measuring thousands of unique sites, includes 3 essential
functionalities: (1) seamless integration with design layout and locations coordinate system; (2) new
design-based pattern recognition and (3) fully automated recipe generation. Additionally advanced
SEM metrology algorithms are required for complex 2-dimensional features, Line-Edge-Roughness
(LER), etc. In this paper, we consider the overall DBM flow, its integration with traditional CDSEM
metrology and the state-of-the-art in recipe automation success. We also investigate advanced
DFM applications, specifically enabled by DBM, particularly for OPC model calibration and
verification, design-driven RET development and parametric Design Rule evaluation and selection.
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