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In this paper, we describe a method to model these localized silicon distortions for complex layouts involving billions of deep trench structures. We describe wafer metrology techniques and data which have been used to verify the stress distortion model accuracy. We also provide a description of how this kind of model can be used to manipulate the polygons in the mask tape out flow to compensate for predicted localized misalignments between design shapes from a deep trench mask and subsequent masks.
In this paper we will present an efficient way to classify and disposition EUV mask defects through a new algorithm developed to classify defects located on EUV photomasks. By processing scanning electronmicroscopy images (SEM) of small regions of a photomask, we extract highdimensional local features Histograms of Oriented Gradients (HOG). Local features represent image contents compactly for detection or classification, without requiring image segmentation. Using these HOGs, a supervised classification method is applied which allows differentiating between nondefective and defective images. In the new approach we have developed a superior method of detection and classification of defects, using mask and supporting mask printed data from several metallization masks. We will demonstrate that use of the HOG method allows realtime identification of defects on EUV masks regardless of geometry or construct.
The defects identified by this classifier are further divided into subclasses for mask defect disposition: foreign material, foreign material from previous step, and topological defects. The goal of disposition is to categorize on the images into subcategories and provide recommendation of prescriptive actions to avoid impact on the wafer yield.
Better on wafer performance and mask manufacturability of contacts with no or non-traditional serifs
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