New resist materials are necessary to achieve higher resolution for the high NA EUV tools. The feature size shrinkage also increases the possibility of defect generation. Therefore, controlling defects remains essential. There are many factors in the lithography process that can contribute to the formation of defects in resist patterns. As a result, when testing the new resist material for patterning, there are more instances of pattern failures than successful ones. However, understanding pattern flaws can gain knowledge about the mechanism of defect generation. Based on the idea that exploiting the information in pattern failures can guide the resist resolution improvement, this study presents a novel method of interpreting patterns with defects based on an image recognition technology named Hough transformation. Approximate 2500 SEM images and part of corresponding simulation results were automatically analyzed. These results were then utilized to extract chemical information.
Traditional resist materials have faced challenges as the extreme ultraviolet (EUV) light source with a wavelength of 13.5 nm brought the evolution of lithography to the semiconductor industry. A significant issue in the development of resist materials or the discovery of new type resists is that numerous parameters involved in the resist pattern printing process cause the generation of defects. Meanwhile, the inherent chemical variation in resist materials and processes causes the stochastic defects. In addition, the stochastic defects caused by the inherent chemical variation in resist materials and processes become increasingly significant as feature scales continue to shrink. Consequently, the number of pattern data with failures is much greater than those without defects. However, by utilizing the information contained in pattern failures, chemical parameters can be adjusted to improve resist resolution. In this study, a new method is proposed for evaluating resist patterns with defects by fitting the experimental scanning electronic microscopy (SEM) images of line-and-space patterns with defects to simulated images.
The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.
We propose a dense motion analysis method for ultrasound images. A motion analysis is implemented by tracking a lot of
lattice points. In this paper, two novel processings are introduced to perform the motion analysis. One is the tracking of
lattice points based on an optical flow algorithm in a framework of multiple spring-models. The other is the detection of
lattice points based on texture information with confidence value, and its result corrects the tracking errors. We evaluated
our method using a sequence of artificial ultrasound images up to 5 minutes. The average and maximum errors of our
proposed method have achieved the best performance in the conventional methods.
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