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10 results for “nanophotonics”
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>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 directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</p>
Plasma etching for fabrication of complex nanophotonic lasers from bonded InP semiconductor layers
<p>Integrating optically active III-V materials on silicon/insulator platforms is one potential path towards improving the energy efficiency and performance of modern computing. Here we demonstrate the applicability of direct wafer bonding combined with plasma etching to the fabrication of complex nanophotonic systems out of InP layers. We explore and optimise the plasma etching of InP, validating existing processes and developing improved ones. We explore the use of microdisk lasing as a way to evaluate fabrication fidelity, and demonstrate that we can create complex lasing systems of interest to us: coupled disk cavities and random network lasers.</p> <p><strong>This repository contains data used to generate figures in <a href="https://doi.org/10.1016/j.mne.2023.100196">https://doi.org/10.1016/j.mne.2023.100196</a>.</strong></p>
Data and simulations files for the tutorial article "Brillouin Optomechanics in Nanophotonic Structures"
<p>Data and simulations files for the tutorial article "Brillouin optomechanics in nanophotonic structures".<br> Published in APL Photonics Special issue "Optoacoustics—Advances in high-frequency optomechanics and Brillouin scattering" - DOI: 10.1063/1.5088169</p>
Inverse Design of Nanophotonic Solid-State Quantum Emitter Single-photon Sources: Data
<p>Data regarding the results presented in the paper "Inverse Design of Nanophotonic Solid-State Quantum Emitter Single-photon Sources".</p>
Data and code for "A Library of Late Transition Metal Alloy Dielectric Functions for Nanophotonic Applications"
<p>This data set contains dielectric functions for the ten binary alloys comprised of the late transition metals most commonly employed in plasmonics (Ag, Au, Cu, Pd, Pt).</p>
Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy - Dataset and Codes
<p>Here we publish the source data and the codes for simulation and data processing regarding the article "Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy" that is published on Optica (<a href="https://doi.org/10.1364/OPTICA.396542">https://doi.org/10.1364/OPTICA.396542</a>).</p>
Supporting data: Ultraviolet astronomical spectrograph calibration with laser frequency combs from nanophotonic lithium niobate waveguides
<p>Data and code to create figures contained in the manuscript "Ultraviolet astronomical spectrograph calibration with laser frequency combs from nanophotonic lithium niobatewaveguides"</p>
Coherent nanophotonic electron accelerator - dataset
<p>Dataset with electron spectra and related quantities shown in Figure 2 of Coherent Nanophotonic Electron Accelerator, Chlouba et al., Nature 2023.</p>
Innovative Drug Delivery Nanophotonic Platform for Implementation of Sarcoma Therapy
ClinicalTrials.gov study NCT06599957. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Source data for "Fast multi-source nanophotonic simulations using augmented partial factorization"
<p>Source data for Fig. 3b-c and Fig. 5a-b</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.