About this role
- Core research - Resources & Collaborations Work Activities Lensless imaging and computational imaging are techniques to reconstruct the shape of an object by measuring its diffraction pattern for a diversity of illuminations, as opposed to using imaging optics as one does in a microscope to create a real space image. While it is well understood how to do this for weakly scattering objects, reconstruction becomes very hard for strongly scattering objects, such as encountered in semiconductor metrology. While strong scattering is problematic for general object reconstruction, at the same time you can also use it as a resource: for instance we showed in a recent paper that you can engineer metasurface resonances to boost sensitivity of diffraction patterns to certain geometrical changes [Nat Commun. 16, 11388]. In this project you will push the boundaries of algorithm based reconstruction of nanoscale strongly scattering geometries from their radiation pattern. We seek to develop a physics-based rational approach to optimal parameter reconstruction from diffraction data from designer multiple scattering structures, designed for, e.g., measuring lithography performance in semiconductor manufacturing. The main idea is that you don't need an algorithm to reconstruct a geometry from scratch, but that you need the algorithm to reconstruct geometry differences relative to a nominal geometry, making full use of physics-based insight, such as the resonant mode structure of the nominal geometry. In this project you will perform measurements and computational reconstruction hand in hand. On the measurement side, you will use a Fourier microscope where you can measure radiation patterns while cycling through a diversity of illuminating wavefronts that you can generate using a spatial light modulator. On the reconstruction side, you will focus on physics-informed algorithms that use the eigenmode structure of the scattering system for reco...