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.
We attempt the problem of autonomous surveillance for person re-identification. This is an active research area, where most recent work focuses on the open challenges of re-identification, independently of prerequisites of detection and tracking. In this paper, we are interested in designing a complete surveillance system, joining all the pieces of the puzzle together. We start by collecting our own dataset from multiple cameras. Then, we automate the process of detection and tracking of human subjects in the scenes, followed by performing the re-identification task. We evaluate the recognition performance of our system, report its strengths, discuss open challenges and suggest ways to address them.
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