Advanced photomasks exploit complex patterns that show little resemblance to the target printed wafer
pattern. The main mask pattern is modified by various OPC and SRAF features while further complexity is
introduced as source-mask-optimization (SMO) technologies experience early adoption at leading
manufacturers. The small size and irregularity of these features challenge the mask inspection process as well
as the mask manufacturing process.
The two major concerns for mask inspection and qualification efficacy of advanced masks are defect
detection and photomask inspectability. Enhanced defect detection is critical for the overall mask
manufacturing process qualification which entails characterization of the systematic deviations of the pattern.
High resolution optical conditions are the optimal solution for manufacturing process qualification as well as
a source of additional information for the mask qualification. Mask inspection using high resolution
conditions operates on an optical image that differs from the aerial image. The high resolution image closely
represents the mask plane pattern. Aerial imaging mode inspection conditions, where the optics of the
inspection tool emulates the lithography manufacturing conditions in a scanner, are the most compatible
imaging solution for photomask pattern development and hence mask inspectability. This is an optimal
environment for performing mask printability characterization and qualification.
In this paper we will compare the roles of aerial imaging and high resolution mask inspection in the mask
The study deals with the challenging task of automatic segmentation of MR brain images with multiple sclerosis lesions
(MSL). Multi-Channel data is used, including "fast fluid attenuated inversion recovery" (fast FLAIR or FF), and
statistical modeling tools are developed, in order to improve cerebrospinal fluid (CSF) classification and to detect MSL.
Two new concepts are proposed for use within an EM framework. The first concept is the integration of prior knowledge
as it relates to tissue behavior in different MRI modalities, with special attention given to the FF modality. The second
concept deals with running the algorithm on a subset of the input that is most likely to be noise- and artifact-free data.
This enables a more reliable learning of the Gaussian mixture model (GMM) parameters for brain tissue statistics. The
proposed method focuses on the problematic CSF intensity distribution, which is a key to improved overall segmentation
and lesion detection. A level-set based active contour stage is performed for lesion delineation, using gradient and shape
properties combined with previously learned region intensity statistics. In the proposed scheme there is no need for preregistration of an atlas, a common characteristic in brain segmentation schemes. Experimental results on real data are