| Literature DB >> 34821030 |
Philipp Hönicke1, Yves Kayser1, Konstantin V Nikolaev2, Victor Soltwisch1, Jeroen E Scheerder3, Claudia Fleischmann3,4, Thomas Siefke5, Anna Andrle1, Grzegorz Gwalt6, Frank Siewert6, Jeffrey Davis7, Martin Huth8, Anabela Veloso4, Roger Loo4, Dieter Skroblin1, Michael Steinert5, Andreas Undisz9, Markus Rettenmayr10, Burkhard Beckhoff1.
Abstract
The spatial and compositional complexity of 3D structures employed in today's nanotechnologies has developed to a level at which the requirements for process development and control can no longer fully be met by existing metrology techniques. For instance, buried parts in stratified nanostructures, which are often crucial for device functionality, can only be probed in a destructive manner in few locations as many existing nondestructive techniques only probe the objects surfaces. Here, it is demonstrated that grazing exit X-ray fluorescence can simultaneously characterize an ensemble of regularly ordered nanostructures simultaneously with respect to their dimensional properties and their elemental composition. This technique is nondestructive and compatible to typically sized test fields, allowing the same array of structures to be studied by other techniques. For crucial parameters, the technique provides sub-nm discrimination capabilities and it does not require access-limited large-scale research facilities as it is compatible to laboratory-scale instrumentation.Entities:
Keywords: dimensional and compositional analysis; grazing exit X-ray fluorescence; machine learning; nanostructure characterization
Mesh:
Year: 2021 PMID: 34821030 DOI: 10.1002/smll.202105776
Source DB: PubMed Journal: Small ISSN: 1613-6810 Impact factor: 13.281