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Background: In the semiconductor industry, CD-SEM (Critical Dimension Scanning Electron Microscope) are key metrology tools used in the control of integrated circuit manufacturing processes. In recent years, several approaches have been evaluated to better monitor these measurement tools from the images generated. One of the promising methodologies is the image sharpness score calculated from the grey levels present on an image. The industrialization of this metric is a major and essential challenge in the CD-SEM image quality monitoring improvement, particularly in the detection of blurred images that can cause erroneous CD (Critical Dimension) measurements. However, the implementation of such an indicator can be complex due to the number of different tool generations and the different beam conditions that can co-exist within the same factory. The diversity of technologies, patterns type, layers and materials add an additional constraint to its industrialization. Aim: This study presents the work done from sharpness score extracted from CD-SEM images to industrialize this score as a metrology indicator. It is based on analyses performed in a production environment comprising a diversity of 4 CD-SEM generations (18 tools) and various products. The objective is to define grouping rules allowing the industrialization of this metric with the aim of detecting equipment drift while offering a better piloting of the metrology equipment capabilities. The interest in this automation will also be to improve out-of-control events treatment efficiency, which are today dependent on human appreciation of image quality. Approach: To systematically save the images and calculate the image sharpness score in real time, a database system was used, coupled with a Virtual Machine (VM) on which software from Aselta Nanographics was installed. The IQ (Image Quality) module of this software was used in this study capable of providing a score on any CD-SEM image without the need to perform any edge detection or pre-calibration. This Image Lab computing infrastructure has enabled the extraction of image sharpness score from over 35 million CD-SEM images collected in addition to the CD measurements already provided by the measurement equipment. Results: The industrialization of the sharpness score required the evaluation and study of the different parameters that could influence this metric. An overview of these results is presented showing a dependence of the image sharpness score on the different pixel sizes that can be used in CDSEM recipes. The use of different materials can also lead to additional sensitivity and variability which may be different depending on the type of CD-SEM used. Further results will be given to better understand the behavior of the sharpness score and to highlight situations that could not be detected in time currently. Conclusion: The analysis of the image sharpness scores revealed the complexity of industrializing this metric and the need to create smart groupings by context such as pattern type, pattern density, material/process type, pixel size and measurement conditions. Adjusting the right sensitivity to the detection of the sharpness score also appeared to be one of the other key criteria in its industrialization. The evaluation of the contour-based sharpness score could provide an answer in the ease of grouping certain contexts.
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