The research and development steps in the semiconductor industry require tools that are able to handle features with large variation across the images, but also tools that can reproduce the definition of an edge taught by an expert. This definition should be easily modified to mimic the expert decisions in order to reduce the time spent by process engineers during research and development phases. We developed a patterned edge model allowing to detect the profile of patterned objects in microscopic images. A complementary tool is proposed to customize the definition between two materials according to the expert targets. The obtained profiles serve as a basis to perform robust metrology and ensure quality control of the manufactured semiconductor components.
KEYWORDS: 3D metrology, Machine learning, Scanning electron microscopy, 3D image processing, Process engineering, Transmission electron microscopy, Image processing, 3D modeling, Semiconductors, Computer simulations
We present a machine learning-based metrology pipeline for electron microscope imagery in the semiconductor industry. The pipeline is targeted to reduce the time spent by Process Engineers during research and development, by automating measurements of features according to their instructions in the form of a “measurement recipe”. Specifically, we present the principles and functionality of tools to measure Fin and 3D Memory structures based on edge finding algorithms, including through direct modelling of the SEM acquisition process to better capture blurred-appearing features.
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