Publications by authors named "Matthias Schlottbom"
State-of-the-art computational methods combined with common idealized structural models provide an incomplete understanding of experimental observations on real nanostructures, since manufacturing introduces unavoidable deviations from the design. We propose to close this knowledge gap by using the real structure of a manufactured nanostructure as input in computations to obtain a realistic comparison with measurements on the same nanostructure. We demonstrate this approach on the structure of a real inverse woodpile photonic bandgap crystal made from silicon, as previously obtained by synchrotron X-ray imaging.
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- The authors introduce a method that uses unsupervised machine learning to improve the classification of wave confinement dimensionality, building on their previous work.
- They apply k-means++ and a new model-based algorithm to analyze 3D superlattices of cavities in a photonic band gap crystal and compare the results with direct scaling methods.
- The study finds that using direct scaling first to identify dimensionality, followed by their model-based clustering, yields the most accurate results.
Functional defects in periodic media confine waves-acoustic, electromagnetic, electronic, spin, etc.-in various dimensions, depending on the structure of the defect. While defects are usually modeled by a superlattice with a typical band-structure representation of energy levels, determining the confinement associated with a given band is highly nontrivial and no analytical method is known to date.
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