Application of Deep Learning to Coherent Fourier Scatterometry data

This thesis discusses the application of deep learning to Coherent Fourier<br/>Scatterometry data in order to quickly and reliably detect nanoparticles on<br/>surfaces. An introduction to deep learning is followed by a review of the<br/>experimental setup and used software. After that, results are presented of<br/>classification accuracy tests...

PINN inspired Freeform Design: Using Fraunhofer Diffraction to find Freeforms described by B-spline Surfaces

This project aims to recreate intensity patterns using Fraunhofer diffraction as a means of simulation. These intensity patterns are created by phase shifting specific parts of an incoming field of light. These phase shifts are determined by a B-spline surface, which is in turn controlled by so-called control points. Only a handful of control...