Due to their increasingly complex 3D geometries, upcoming gate all around (GAA) devices pose new metrology challenges for which there is not yet any established HVM metrology solution, in particular for various critical timed etch steps [5]. Soft x-ray (SXR) scatterometry using 10-20 nm wavelength light is a promising next-generation metrology technique for 3D profile metrology and overlay (OVL) applications. This wavelength regime offers unique benefits over existing metrology techniques today: (1) Short wavelengths allow for higher resolution measurements than traditional visible wavelengths could offer, enabling measurement of structures at device pitches. (2) Primarily single scattering yields low correlation between parameters and aids physical interpretation of signals. This enables many parameters of interest to be extracted accurately and simultaneously. (3) SXR provides 3D capability, with stack heights up to 400 nm supported and high depth resolution due to the broadband source and sensor. These properties together make SXR suitable for measuring the 3D profiles of advanced devices such as gate all around (GAA) transistors, as well as after develop (ADI) overlay at device pitch. In this paper, we demonstrate SXR for profile metrology of GAA devices. We show sensitivity to average SiGe lateral recess etch depth as well as individual nanosheet critical dimensions, which cannot be reliably accessed by other nondestructive, inline metrology techniques available today. We furthermore demonstrate sensitivity in ADI OVL measurements directly on device-pitch structures in the presence of an underlying patterned nuisance layer.
way. -Different 3,5-diazido-3,5-dideoxy and 2,3,5-trideoxyfuranoses, (VI), (XII), and (XVIII) both with xylo-and ribo-configurations, are prepared by different approaches thus overcoming the drawbacks of known procedures such as lacking the availability of cheap starting materials, adequate yields, and access to all possible diastereomers. The new methods applied offer new possibilities for the synthesis of still unknown artificial nucleosides and chiral metal complexes. -(KOTH, D.; FIEDLER, A.; SCHOLZ, S.; GOTTSCHALDT*, M.; J. Carbohydr. Chem. 26 (2007) 4-6, 267-278; Inst. Org. Makromol. Chem., Friedrich-Schiller-Univ.,
Many nonequilibrium systems, such as biochemical reactions and socioeconomic interactions, can be described by reaction–diffusion equations that demonstrate a wide variety of complex spatiotemporal patterns. The diversity of the morphology of these patterns makes it difficult to classify them quantitatively, and they are often described visually. Hence, searching through a large parameter space for patterns is a tedious manual task. We discuss how convolutional neural networks can be used to scan the parameter space, investigate existing patterns in more detail, and aid in finding new groups of patterns. As an example, we consider the Gray–Scott model for which training data are easy to obtain. Due to the popularity of machine learning in many scientific fields, well maintained open source toolkits are available that make it easy to implement the methods that we discuss in advanced undergraduate and graduate computational physics projects.
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