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Source code and simulation results: Computing eigenfrequency sensitivities near exceptional points
(2024)
Optimized Sensing on Gold Nanoparticles Created by Graded-Layer Magnetron Sputtering and Annealing
(2024)
Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna
(2024)
Modelling luminescent coupling in multi-junction solar cells: perovskite silicon tandem case study
(2024)
Machine-learning driven design of metasurfaces: learn the physics and not the objective function
(2024)
Cover Picture: Resonance Expansion of Quadratic Quantities with Regularized Quasinormal Modes
(2023)
In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence x-ray fluorescence data set in order to obtain the geometrical parameters of a nano structured line grating. Gaussian process, stochastic machine learning surrogate models, were trained during the reconstruction and afterwards sampled with a Markov chain Monte Carlo sampler to determine the distribution of the reconstructed model parameters. The numerical discretization parameters of the used finite element model impact the numerical discretization error of the forward model. We investigated the impact of the polynomial order of the finite element ansatz functions on the reconstructed parameters as well as on the model parameter distributions. We showed that such a convergence study allows to determine numerical parameters which allows for efficient and accurate reconstruction results.
RPExpand (Version 2.0)
(2023)
RPExpand (Version 1)
(2023)
Tripling the light extraction efficiency of a deep ultraviolet LED using a nanostructured p-contact
(2022)