arXiv subjects

Philipp-Immanuel Schneider

Publications and source records attributed to Philipp-Immanuel Schneider.

At least 19 recordsLinked to original sources

arXiv2025

High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers

We propose a nanobeam resonator incorporating an active monolayer, designed to achieve a high Purcell enhancement. The resonator is fully composed of transition-metal-dichalcogenide materials and intended to operate as a high-beta-factor nanolaser. A theoretical framework that models and optimizes the Purcell enhancement associated with the emission from atomically thin layers is developed. This framework is based on a resonance expansion, enabling spectral resolution of physical quantities governed by high-Q resonances. The numerical optimization of the resonator leads to the presence of a high-Q resonance supporting a strong electric field confinement in the monolayer to maximize the modal gain.

physics.optics↗

arXiv2024

Bayesian optimization for state engineering of quantum gases

State engineering of quantum objects is a central requirement in most implementations. In the cases where the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems, however, such as multi-component quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the quantum state's physical properties from few simulations only. We evaluate its performance on an optimization study case of diabatically transporting a Bose-Einstein condensate while keeping it in its ground state, and show that within only few hundreds of executions of the underlying physics simulation, we reach a competitive performance with other protocols. While restricting this benchmarking to well known approximations for straightforward comparisons, we expect a similar performance when employing more involving models, which are computationally more challenging. This paves the way to efficient state engineering of complex quantum systems.

quant-ph↗

arXiv2024

Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes

We present a fabrication uncertainty aware and robust design optimization approach that can be used to obtain robust design estimates for nonlinear, nonconvex, and expensive model functions. It is founded on Gaussian processes and a Monte Carlo sampling procedure, and assumes knowledge about the uncertainties associated with a manufacturing process. The approach itself is iterative. First, a large parameter domain is sampled in a coarse fashion. This coarse sampling is used primarily to determine smaller candidate regions to investigate in a second, more refined sampling pass. This finer step is used to obtain an estimate of the expected performance of the found design parameter under the assumed manufacturing uncertainties. We apply the presented approach to the robust optimization of the Purcell enhancement of a photonic crystal nanobeam cavity. We obtain a predicted robust Purcell enhancement of $\overline{F}_{\mathrm{P}} \approx 3.3$. For comparison we also perform an optimization without robustness. We find that an unrobust optimum of $F_{\mathrm{P}} \approx 256.5$ dwindles to only $\overline{F}_{\mathrm{P}} \approx 0.7$ when fabrication uncertainties are taken into account. We thus demonstrate that the presented approach is able to find designs of significantly higher performance than those obtained with conventional optimization.

eess.SP↗

arXiv2023

Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments

The generation of cold atom clouds is a complex process which involves the optimization of noisy data in high dimensional parameter spaces. Optimization can be challenging both in and especially outside of the lab due to lack of time, expertise, or access for lengthy manual optimization. In recent years, it was demonstrated that machine learning offers a solution since it can optimize high dimensional problems quickly, without knowledge of the experiment itself. In this paper we present results showing the benchmarking of nine different optimization techniques and implementations, alongside their ability to optimize a Rubidium (Rb) cold atom experiment. The investigations are performed on a 3D $^{87}$Rb molasses with 10 and 18 adjustable parameters, respectively, where the atom number obtained by absorption imaging was chosen as the test problem. We further compare the best performing optimizers under different effective noise conditions by reducing the Signal-to-Noise ratio of the images via adapting the atomic vapor pressure in the 2D+ MOT and the detection laser frequency stability.

physics.atom-ph↗

arXiv2023

Forward simulation of coherent beams on grating structures for coherent scatterometry

Modelling the scattering of focused, coherent light by nano-scale structures is oftentimes used to reconstruct or infer geometrical or material properties of structures under investigation in optical scatterometry. This comprises both periodic and aperiodic nano-structures. Coherent Fourier scatterometry with focused light exploits the diffraction pattern formed by the nano-structures in Fourier plane. While the scattering of a focused beam by a spatially isolated scatterer is a standard modelling task for state-of-the art electromagnetic solvers based, e.g., on the finite element method, the case of periodically structured samples is more involved. In particular when the focused light covers several grating periods of as it is commonly the case. We will present a coherent illumination model for scattering of focused beams such as Gaussian -- and Bessel -- beams by periodic structures such as line gratings. The model allows to take into account optical wavefront aberrations in optical systems used for both, the illumination and detection of the scattered fields. We compare the model with strategies implemented on large-scale super-cells and inverse Floquet-transform strategies to superimpose both near- and far- fields coherently.

physics.optics↗

arXiv2023

Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions

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.

physics.comp-ph↗

arXiv2023

Applying a Riesz-projection-based contour integral eigenvalue solver to compute resonance modes of a VCSEL

We investigate the calculation of resonance modes of a VCSEL with a Riesz projection eigenvalue solver. The eigenvalue solver is based on the principle of contour integration where for the solution of scattering problems physical right sides are used. Here, it is investigated how numerical parameters impact the performance of the method, where we focus on the computation of the fundamental VCSEL mode.

physics.comp-ph↗

arXiv2022

Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators

Resonances are omnipresent in physics and essential for the description of wave phenomena. We present an approach for computing eigenfrequency sensitivities of resonances. The theory is based on Riesz projections and the approach can be applied to compute partial derivatives of the complex eigenfrequencies of any resonance problem. Here, the method is derived for Maxwell's equations. Its numerical realization essentially relies on direct differentiation of scattering problems. We use a numerical implementation to demonstrate the performance of the approach compared to differentiation using finite differences. The method is applied for the efficient optimization of the quality factor of a nanophotonic resonator.

physics.comp-ph↗

arXiv2022

Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction

Parameter reconstructions are indispensable in metrology. Here, the objective is to to explain $K$ experimental measurements by fitting to them a parameterized model of the measurement process. The model parameters are regularly determined by least-square methods, i.e., by minimizing the sum of the squared residuals between the $K$ model predictions and the $K$ experimental observations, $χ^2$. The model functions often involve computationally demanding numerical simulations. Bayesian optimization methods are specifically suited for minimizing expensive model functions. However, in contrast to least-square methods such as the Levenberg-Marquardt algorithm, they only take the value of $χ^2$ into account, and neglect the $K$ individual model outputs. We present a Bayesian target-vector optimization scheme with improved performance over previous developments, that considers all $K$ contributions of the model function and that is specifically suited for parameter reconstruction problems which are often based on hundreds of observations. Its performance is compared to established methods for an optical metrology reconstruction problem and two synthetic least-squares problems. The proposed method outperforms established optimization methods. It also enables to determine accurate uncertainty estimates with very few observations of the actual model function by using Markov chain Monte Carlo sampling on a trained surrogate model.

physics.comp-ph↗

arXiv2021

Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology

Parameter reconstruction is a common problem in optical nano metrology. It generally involves a set of measurements, to which one attempts to fit a numerical model of the measurement process. The model evaluation typically involves to solve Maxwell's equations and is thus time consuming. This makes the reconstruction computationally demanding. Several methods exist for fitting the model to the measurements. On the one hand, Bayesian optimization methods for expensive black-box optimization enable an efficient reconstruction by training a machine learning model of the squared sum of deviations. On the other hand, curve fitting algorithms, such as the Levenberg-Marquardt method, take the deviations between all model outputs and corresponding measurement values into account which enables a fast local convergence. In this paper we present a Bayesian Target Vector Optimization scheme which combines these two approaches. We compare the performance of the presented method against a standard Levenberg-Marquardt-like algorithm, a conventional Bayesian optimization scheme, and the L-BFGS-B and Nelder-Mead simplex algorithms. As a stand-in for problems from nano metrology, we employ a non-linear least-square problem from the NIST Standard Reference Database. We find that the presented method generally uses fewer calls of the model function than any of the competing schemes to achieve similar reconstruction performance.

physics.comp-ph↗

arXiv2021

Optimized diamond inverted nanocones for enhanced color center to fiber coupling

Nanostructures can be used for boosting the light outcoupling of color centers in diamond; however, the fiber coupling performance of these nanostructures is rarely investigated. Here, we use a finite element method for computing the emission from color centers in inverted nanocones and the overlap of this emission with the propagation mode in a single-mode fiber. Using different figures of merit, the inverted nanocone parameters are optimized to obtain maximal fiber coupling efficiency, free-space collection efficiency, or rate enhancement. The optimized inverted nanocone designs show promising results with 66% fiber coupling or 83% free-space coupling efficiency at the tin-vacancy center zero-phonon line wavelength of 619 nm. Moreover, when evaluated for broadband performance, the optimized designs show 55% and 76% for fiber coupling and free-space efficiencies respectively, for collecting the full tin-vacancy emission spectrum at room temperature. An analysis of fabrication insensitivity indicates that these nanostructures are robust against imperfections. For maximum emission rate into a fiber mode, a design with a Purcell factor of 2.34 is identified. Finally, possible improvements offered by a hybrid inverted nanocone, formed by patterning into two different materials, are investigated, and increases the achievable fiber coupling efficiency to 71%.

quant-ph↗

arXiv2021

Shape- and element-sensitive reconstruction of periodic nanostructures with grazing incidence X-ray fluorescence analysis and machine learning

The characterization of nanostructured surfaces with sensitivity in the sub-nm range is of high importance for the development of current and next generation integrated electronic circuits. Modern transistor architectures for e.g. FinFETs are realized by lithographic fabrication of complex, well ordered nanostructures. Recently, a novel characterization technique based on X-ray fluorescence measurements in grazing incidence geometry has been proposed for such applications. This technique uses the X-ray standing wave field, arising from an interference between incident and the reflected radiation, as a nanoscale sensor for the dimensional and compositional parameters of the nanostructure. The element sensitivity of the X-ray fluorescence technique allows for a reconstruction of the spatial element distribution using a finite-element method. Due to a high computational time, intelligent optimization methods employing machine learning algorithms are essential for a timely provision of results. Here, a sampling of the probability distributions by Bayesian optimization is not only fast, it also provides an initial estimate of the parameter uncertainties and sensitivities. The high sensitivity of the method requires a precise knowledge of the material parameters in the modeling of the dimensional shape provided that some physical properties of the material are known or determined beforehand. The unknown optical constants were extracted from an unstructured but otherwise identical layer system by means of soft X-ray reflectometry. The spatial distribution profiles of the different elements contained in the grating structure were compared to scanning electron and atomic force microscopy and the influence of carbon surface contamination on the modeling results were discussed.

physics.app-ph↗

arXiv2021

Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures

We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.

physics.comp-ph↗

arXiv2020

Efficient Bayesian inversion for shape reconstruction of lithography masks

Background: Scatterometry is a fast, indirect and non-destructive optical method for quality control in the production of lithography masks. To solve the inverse problem in compliance with the upcoming need for improved accuracy, a computationally expensive forward model has to be defined which maps geometry parameters to diffracted light intensities. Aim: To quantify the uncertainties in the reconstruction of the geometry parameters, a fast to evaluate surrogate for the forward model has to be introduced. Approach: We use a non-intrusive polynomial chaos based approximation of the forward model which increases speed and thus enables the exploration of the posterior through direct Bayesian inference. Additionally, this surrogate allows for a global sensitivity analysis at no additional computational overhead. Results: This approach yields information about the complete distribution of the geometry parameters of a silicon line grating, which in return allows to quantify the reconstruction uncertainties in the form of means, variances and higher order moments of the parameters. Conclusion: The use of a polynomial chaos surrogate allows to quantify both parameter influences and reconstruction uncertainties. This approach is easy to use since no adaptation of the expensive forward model is required.

physics.data-an↗

arXiv2019

Plug&play fibre-coupled 73 kHz single-photon source operating in the telecom O-band

A user-friendly fibre-coupled single-photon source operating at telecom wavelengths is a key component of photonic quantum networks providing long-haul ultra-secure data exchange. To take full advantage of quantum-mechanical data protection and to maximize the transmission rate and distance, a true quantum source providing single-photons on demand is highly desirable. We tackle this great challenge by developing a ready to use semiconductor quantum dot (QD)-based device that launches single photons at a wavelength of 1.3 um directly into a single-mode optical fibre. In our approach the QD is deterministically integrated into a nanophotonic structure to ensure efficient on-chip coupling into a fibre. The whole arrangement is integrated into a 19" compatible housing to enable stand-alone operation by cooling via a compact Stirling cryocooler. The realized source delivers single photons with multiphoton events probability as low as 0.15 and single-photon emission rate up to 73 kHz into a standard telecom single-mode fibre.

physics.app-ph↗

arXiv2019

Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general non-convex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems: \removed{downhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization} \added{particle swarm optimization, differential evolution, and Bayesian optimization as well as multi-start versions of downhill simplex optimization and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm}. In the shown examples from the field of shape optimization and parameter reconstruction, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.

physics.comp-ph↗

arXiv2019

An efficient approach to global sensitivity analysis and parameter estimation for line gratings

Scatterometry is a fast, indirect and nondestructive optical method for the quality control in the production of lithography masks. Geometry parameters of line gratings are obtained from diffracted light intensities by solving an inverse problem. To comply with the upcoming need for improved accuracy and precision and thus for the reduction of uncertainties, typically computationally expansive forward models have been used. In this paper we use Bayesian inversion to estimate parameters from scatterometry measurements of a silicon line grating and determine the associated uncertainties. Since the direct application of Bayesian inference using Markov-Chain Monte Carlo methods to physics-based partial differential equation (PDE) model is not feasible due to high computational costs, we use an approximation of the PDE forward model based on a polynomial chaos expansion. The expansion provides not only a surrogate for the PDE forward model, but also Sobol indices for a global sensitivity analysis. Finally, we compare our results for the global sensitivity analysis with the uncertainties of estimated parameters.

physics.data-an↗

arXiv2019

Using Gaussian process regression for efficient parameter reconstruction

Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. We examine how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.

physics.comp-ph↗