Deep Learning for Optimization of Spatial Light Modulators
Spatial Light Modulators can be used to modulate the effective shape of light, e.g., a laser beam. They are thus useful in industrial applications like laser cutting where beam shapes need to be adapted quickly. However, generating complex shapes is time-consuming and error-prone using current algorithms based on Fourier transformations. In this work, a deep learning approach that automatically generates the correct modulations to obtain the desired shape should be explored. The Thesis is conducted in cooperation with the company LIDROTEC(https://www.lidrotec.de/).