We introduce the value-and-criterion filter structure, a new framework for designing filters based on mathematical morphology. The value-and-criterion filter structure is more flexible than the morphological structure, because it allows linear and nonlinear operations other than just the minimum and maximum to be performed on the data. One particular value-and-criterion filter, the Mean of Least Variance (MLV) filter, finds the mean over the "subwindow" of data with the smallest variance within an overall window. The ability of the MLV filter to smooth noise while preserving and enhancing edges and corners is demonstrated. An example application of the MLV filter in improving the contrast of magnetic resonance images is also shown.
Defect inspection metrology is an integral part of the yield ramp and process monitoring phases of semiconductor manufacturing. High aspect ratio structures have been identified in the ITRS as critical structures where there are no known manufacturable solutions for defect detection. We present case studies of a new inspection technology based on digital holography that addresses this need. Digital holography records the amplitude and phase of the wavefront from the target object directly to a single image acquired by a CCD camera. Using deep ultraviolet laser illumination, digital holography is capable of resolving phase differences corresponding to height differences as small as several nanometers. Thus, the technology is well suited to the task of finding defects on semiconductor wafers. We present a study of several defect detection benchmark wafers, and compare the results of digital holographic inspection to other wafer inspection technologies. Specifically, digital holography allows improved defect detection on high aspect ratio features, such as improperly etched contacts. In addition, the phase information provided by digital holography allows us to visualize the topology of defects, and even generate three-dimensional images of the wafer surface comparable to scanning electron microscope (SEM) images. These results demonstrate the unique defect detection capabilities of digital holography.
Background. The discriminatory power and imaging efficiency of different multicolor FISH (M-FISH) analysis systems are key factors in obtaining accurate and reproducible classification results. In a recent paper, Garini et al. put forth an analytical technique to quantify the discriminatory power ("S/N ratio") and imaging efficiency ('excitation efficiency') of multicolor fluorescent karyotyping systems. Materials and Methods. A parametric model of multicolor fluorescence microscopy, based on the Beer-Lambert law, is analyzed and reduced to a simple expression for S/N ratio. Parameters for individual system configurations are then plugged into the model for comparison purposes. Results. We found that several invalid assumptions, which are used to reduce the complex mathematics of the Beer-Lambert law to a simple S/N ratio, result in some completely misleading conclusions about classification ac-
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