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Machine learning aided solution to the inverse problem in optical scatterometry
Optical scatterometry is the workhorse technique for in-line manufacturing process control in
the semiconductor industry. However, as manufacturing processes develop, traditional …
the semiconductor industry. However, as manufacturing processes develop, traditional …
Simultaneous dimensional and analytical characterization of ordered nanostructures
The spatial and compositional complexity of 3D structures employed in today's
nanotechnologies has developed to a level at which the requirements for process …
nanotechnologies has developed to a level at which the requirements for process …
Global sensitivity analysis and uncertainty quantification for simulated atrial electrocardiograms
The numerical modeling of cardiac electrophysiology has reached a mature and advanced
state that allows for quantitative modeling of many clinically relevant processes. As a result …
state that allows for quantitative modeling of many clinically relevant processes. As a result …
Bayesian Target‐Vector Optimization for Efficient Parameter Reconstruction
M Plock, K Andrle, S Burger… - Advanced Theory and …, 2022 - Wiley Online Library
Parameter reconstructions are indispensable in metrology. Here, the objective is to explain
K experimental measurements by fitting to them a parameterized model of the measurement …
K experimental measurements by fitting to them a parameterized model of the measurement …
Inverse scattering with a parametrized spatial spectral volume integral equation for finite scatterers
S Eijsvogel, RJ Dilz, MC van Beurden - Journal of the Optical Society …, 2023 - opg.optica.org
In wafer metrology, the knowledge of the photomask together with the deposition process
only reveals the approximate geometry and material properties of the structures on a wafer …
only reveals the approximate geometry and material properties of the structures on a wafer …
[PDF][PDF] PyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansions
N Hegemann, S Heidenreich - Journal of Open Source Software, 2023 - joss.theoj.org
PyThia is a Python package for quantifying uncertainties by computing polynomial chaos
surrogates for computationally expensive parametric models (eg, parametric partial …
surrogates for computationally expensive parametric models (eg, parametric partial …
Efficient approximation of high-dimensional exponentials by tensor networks
In this work a general approach to compute a compressed representation of the exponential
exp (h) of a high-dimensional function h is presented. Such exponential functions play an …
exp (h) of a high-dimensional function h is presented. Such exponential functions play an …
Nondestructive measurement of terahertz optical thin films by machine learning based on physical consistency
Z Ming, D Liu, L **ao, L Yang, Y Cheng, H Yang… - Optics …, 2024 - opg.optica.org
Optical scattering measurement is one of the most commonly used methods for non-contact
online measurement of film properties in industrial film manufacturing. Terahertz photons …
online measurement of film properties in industrial film manufacturing. Terahertz photons …
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 …
involves a set of measurements, to which one attempts to fit a numerical model of the …
[КНИГА][B] Adaptive and non-intrusive uncertainty quantification for high-dimensional parametric PDEs
N Farchmin - 2022 - search.proquest.com
Diese Dissertation beschäftigt sich mit der Kombination aus verlässlicher Fehlerkontrolle
und datenbasierter Approximation um nicht-intrusive und zuverlässige Algorithmen zur …
und datenbasierter Approximation um nicht-intrusive und zuverlässige Algorithmen zur …