Extreme ultraviolet lithography (EUV) advances printability of small size features for both memory and logic semiconductor devices. It promises to bring relief to the semiconductor manufacturing industry, removing the need for multiple masks in rendering a single design layer on wafer. However, EUV also brings new challenges, one of which is of mask defectivity. For this purpose, much of the focus in recent years has been in finding ways to adequately detect, characterize, and reduce defects on both EUV blanks and patterned 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.
More complex source and mask shapes are required to maximize the process window in low κ1 era. In simulation, the improvement can be shown well with ideal source and mask shapes. However imperfection of the source and mask can cause critical dimension (CD) errors and results in smaller process margin than expected one. In this paper, it is shown
that how process margins can be improved with different source and mask complexities. Also the effect of source and
mask complexities on CD errors and process margin degradation is discussed. The error source of the electron beam
mask pattern generator is investigated and used for mask CD uniformity estimation with different mask complexity.
Model-based Optical Proximity Correction (OPC) is widely used in advanced lithography processes. The OPC model
contains an empirical part, which is calibrated by fitting the model with data from test patterns. Therefore, the success of
the OPC model strongly relies on a test pattern sampling method.
This paper presents a new automatic sampling method for OPC model calibration, using centroid-based clustering in a
hybrid space: the direct sum of geometrical sensitivity space and image parameter space. This approach is applied to an
example system in order to investigate the minimum size of a sampling set, so that the resulting calibrated model has the
error comparable to that of the model built with a larger sampling set.
The proposed sampling algorithm is verified for the case of a contact layer of the most recent logic device.
Particularly, test patterns with both 1D and 2D geometries are automatically sampled from the layer and then measured
at the wafer level. The subsequent model built using this set of test patterns provides high prediction accuracy.