Sparse image reconstruction techniques have been used to recover high frequency information lost during the acquisition process in different imaging domains, such as ultrasound, synthetic aperture radar, optical microscopy, and astronomical and microscopic imaging. In this work, a signal processing framework is proposed to estimate the Point Spread Function (PSF) of the dark-field subsurface microscopy system from observation data. This PSF is incorporated into an image reconstruction framework, which can be formulated with two different image reconstruction techniques, regularized image reconstruction and dictionary-based image reconstruction. It is observed that both techniques provide at least 12% resolution improvement; lines with 224 nm spacing were localized after resolution improvement while lines with 252 nm spacing are at the limit of localization in experimental data. However, dictionary-based image reconstruction provides higher edge resolution and maintains the homogeneity of the intensity within the structures.

As feature sizes in integrated circuits (ICs) become smaller, higher-resolution defect detection and failure analysis techniques are required. The introduction of solid immersion lenses (SIL) has been an enabling technology for highresolution backside IC imaging. High Numerical Aperture (NA) SIL imaging introduces properties of focused light which cannot be predicted by scalar beam optics. For example, spatial resolution can be manipulated in selected directions by modification of the polarization direction in linearly polarized light. In this work, we propose a unified framework combining multiple SIL microscopy images collected using polarizations at different directions in order to improve image reconstruction performance and ultimately resolution and defect localization. We show improvement in reconstruction quality by combining data taken using light with multiple polarizations. We demonstrate the effectiveness of our framework on experimental data.