Scanning electron microscopy (SEM) has realized high-throughput defect monitoring of semiconductor devices. As miniaturization and complexification of semiconductor circuit patterns increase in recent years, so has the number of defects. There is thus a great need to further increase the throughput of SEM defect monitoring. Toward this end, we propose a deep learning-based super-resolution method that reproduces high-resolution (HR) images from corresponding low-resolution images. Image quality factors such as pattern contrast and sharpness are important in SEM HR images in order to evaluate the quality of printed circuit patterns. Our proposed method meets various image quality requirements by changing the loss calculation method pixelwise based on the pattern in the image. It realizes super-resolved images that compare favorably with actual HR images and can improve SEM throughput by 100% or more.
Keywords: SEM; image to image translation; loss function; machine learning; semiconductor inspection; super-resolution.
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