We present an experimental study of pattern variability and defectivity, based on a large data set with more than 112 million SEM measurements from an HMI high-throughput e-beam tool. The test case is a 10nm node SRAM via array patterned with a DUV immersion LELE process, where we see a variation in mean size and litho sensitivities between different unique via patterns that leads to a seemingly qualitative differences in defectivity. The large available data volume enables further analysis to reliably distinguish global and local CDU variations, including a breakdown into local systematics and stochastics. A closer inspection of the tail end of the distributions and estimation of defect probabilities concludes that there is a common defect mechanism and defect threshold despite the observed differences of specific pattern characteristics. We expect that the analysis methodology can be applied for defect probability modeling as well as general process qualification in the future.
This paper presents an effort that is aimed at addressing two challenges in the monitoring of some bridges
and roads: poor wireless signal transmission and low real-world survivability of wireless sensors. A product is
proposed; a prototype is designed and made to protect some off-the-shelf wireless sensors and improve their
performance. The design is based on numerical simulations of the electromagnetic field including using finitedifference
time-domain method. To protect the wireless sensors and all other components, basic structural
analysis is exercised to obtain an enclosure design that tends to optimize the load-carrying capacity given the
design constraints. All materials used for the enclosure have low electrical conductivities limiting or nullifying
their negative imprint on wireless communication. Off-the-shelf components are utilized as often as possible to
minimize the overall cost and expedite the manufacturing process. The problem encountered in the real-world
testing of the design is presented, analyzed and solved.
Background modeling techniques are important for object detection and tracking in video surveillances. Traditional
background subtraction approaches are suffered from problems, such as persistent dynamic backgrounds, quick
illumination changes, occlusions, noise etc. In this paper, we address the problem of detection and localization of moving
objects in a video stream without apperception of background statistics. Three major contributions are presented. First,
introducing the sequential Monte Carlo sampling techniques greatly reduce the computation complexity while
compromise the expected accuracy. Second, the robust salient motion is considered when resampling the feature points
by removing those who do not move in a relative constant velocity and emphasis those in consistent motion. Finally, the
proposed joint feature model enforced spatial consistency. Promising results demonstrate the potentials of the proposed
algorithm.
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