Proc. SPIE. 11329, Advanced Etch Technology for Nanopatterning IX
KEYWORDS: Semiconductors, Image processing, Computer simulations, 3D modeling, Scanning electron microscopy, Transmission electron microscopy, 3D metrology, Machine learning, 3D image processing, Process engineering
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.