Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
type: Item
instance of: data set
publication date: 2022-08-10T00:00:00Z
author: Felix Binkowski
author: Fridtjof Betz
author: Martin Hammerschmidt
author: Philipp-Immanuel Schneider
author: Lin Zschiedrich
author: Sven Burger
Zenodo ID: 6614951
copyright license: Creative Commons Attribution 4.0 International
community: MATH+
Internal Project ID: AA2-16
description: Summary Data and source code relate to the article Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators [1]. It combines direct differentiation of scattering problems with a contour integral method [2]to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method. Structure The most important elements of this publication are the MATLAB scripts sensitivities.m and optimization.m, which can be used to reproduce the most important results of the paper. The directoriescode,scatteringandresultscontain the software RPExpand[3], input files for JCMsuite [4] and results produced with the scripts, respectively. Furthermore, the latter contains the subfoldertabulated,which contains text filestabulatingdata presentedin Figures 2 and 4 of the paper. Eventually, the function code/observation.m evaluates the target for the optimization. Additional Information The applicaton is based on an example from the literature [5]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to bethe component of the electric field normal to the plane defining the solidof revolution. The convergence studies are based on the discrete, circular contour\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)with center \(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)and radius \(r_0 = \omega_0\times10^{-2}\). For finite element degrees \(d\) higher than 5, the error saturates. For this reason, the differences between results for \(d=5\) and \(d = 6\) may depend on the hardware architecture. A larger radius\(r = 4\times10^{13}\) has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function \(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)is minimized. The first factor is the negative Q-Factor…
dataset version identifier: 1
MaRDI profile type: Dataset
DOI: 10.5281/ZENODO.6614951
description: Dataset published at Zenodo repository.