arXiv subjects

Wouter Van den Broek

Publications and source records attributed to Wouter Van den Broek.

6 recordsLinked to original sources

arXiv2024

Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization

By working out the Bethe sum rule, a boundary condition that takes the form of a linear equality is derived for the fine structure observed in ionization edges present in electron energy-loss spectra. This condition is subsequently used as a constraint in the estimation process of the elemental abundances, demonstrating starkly improved precision and accuracy and reduced sensitivity to the number of model parameters. Furthermore, the fine structure is reliably extracted from the spectra in an automated way, thus providing critical information on the sample's electronic properties that is hard or impossible to obtain otherwise. Since this approach allows dispensing with the need for user-provided input, a potential source of bias is prevented.

cond-mat.mtrl-sci↗

arXiv2023

Convexity constraints on linear background models for electron energy-loss spectra

In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simulated backgrounds, especially for wide energy ranges, and thus improves elemental quantification results. Owing to the model's linearity, the constraints can be imposed through fitting by quadratic programming. This has important advantages over conventional nonlinear power-law fitting such as high speed and a guaranteed unique solution without need for initial parameters. As such, the need for user input is significantly reduced, which is essential for unsupervised treatment of large data sets. This is demonstrated on a demanding spectrum image of a semiconductor device sample with a high number of elements over a wide energy range.

physics.data-an↗

arXiv2022

Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography

We present a method that lowers the dose required for a ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The proposed method is built upon a deep learning model that is trained by reinforcement learning (RL), using prior knowledge of the specimen structure from training data sets. We show that equivalent low-dose experiments using adaptive scanning outperform conventional ptychography experiments in terms of reconstruction resolution.

physics.comp-ph↗

arXiv2020

Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization

The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings. Furthermore, reconstruction of the sample-induced phase shift is typically limited by uncertainty in the experimental parameters and finite sample thicknesses. Presented is a conjugate gradient descent algorithm, regularized optimization for ptychography (ROP), that recovers the partially known experimental parameters along with the phase shift, improves resolution by incorporating the multislice formalism to treat finite sample thicknesses, and includes regularization in the optimization process, thus achieving reliable results from noisy data with severely reduced and underdetermined information.

eess.SP↗

arXiv2019

Various Compressed Sensing Set-Ups Evaluated Against Shannon Sampling Under Constraint of Constant Illumination

Under the constraint of constant illumination, an information criterion is formulated for the Fisher information that compressed sensing measurements in optical and transmission electron microscopy contain about the underlying parameters. Since this approach requires prior knowledge of the signal's support in the sparse basis, we develop a heuristic quantity, the detective quantum efficiency (DQE), that tracks this information criterion well without this knowledge. It is shown that for the investigated choice of sensing matrices, and in the absence of read-out noise, i.e. with only Poisson noise present, compressed sensing does not raise the amount of Fisher information in the recordings above that of Shannon sampling. Furthermore, enabled by the DQE's analytical tractability, the experimental designs are optimized by finding out the optimal fraction of on-pixels as a function of dose and read-out noise. Finally, we introduce a regularization and demonstrate, through simulations and experiment, that it yields reconstructions attaining minimum mean squared error at experimental settings predicted by the DQE as optimal.

physics.ins-det↗

arXiv2016

A Novel Quasi-One-Dimensional Topological Insulator in Bismuth Iodide $β$-Bi$_4$I$_4$

Recent progress in the field of topological states of matter(1,2) has largely been initiated by the discovery of bismuth and antimony chalcogenide bulk topological insulators (TIs)(3-6), followed by closely related ternary compounds(7-16) and predictions of several weak TIs(17-19). However, both the conceptual richness of Z$_2$ classification of TIs as well as their structural and compositional diversity are far from being fully exploited. Here, a new Z$_2$ topological insulator is theoretically predicted and experimentally confirmed in the $β$-phase of quasi-one-dimensional bismuth iodide Bi$_4$I$_4$. The electronic structure of $β$-Bi$_4$I$_4$, characterized by Z$_2$ invariants (1;110), is in proximity of both the weak TI phase (0;001) and the trivial insulator phase (0;000). Our angle-resolved photoemission spectroscopy measurements on the (001) surface reveal a highly anisotropic band-crossing feature located at the point of the surface Brillouin zone and showing no dispersion with the photon energy, thus being fully consistent with the theoretical prediction.

cond-mat.mtrl-sci↗