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Found 9 papers, 0 papers with code
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes
We apply the presented approach to the robust optimization of the Purcell enhancement of a photonic crystal nanobeam cavity.
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.
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.
Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction
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.
Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology
It generally involves a set of measurements, to which one attempts to fit a numerical model of the measurement process.
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.
Applied Physics
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.
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.
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.
