Computational polarized Raman microscopy on sub-surface nanostructures with sub-diffraction-limit resolution

Abstract

Raman microscopy with resolution below the diffraction limit is demonstrated on sub-surface nanostructures. Unlike most other modalities for nanoscale measurements, our approach is able to image nanostructures buried several microns below the sample surface while still extracting details about the chemistry, strain, and temperature of the nanostructures. In this work, we demonstrate that combining polarized Raman microscopy adjusted to optimize edge enhancement effects and nanostructure contrast with fast computational deconvolution methods can improve the spatial resolution while preserving the flexibility of Raman microscopy. The cosine transform method demonstrated here enables significant computational speed-up from O ( N 3 ) to O ( N log N ) - resulting in computation times that are significantly below the image acquisition time. CMOS poly-Si nanostructures buried below 0.3 − 6 µ m of complex dielectrics are used to quantify the performance of the instrument and the algorithm. The relative errors of the feature sizes, the relative chemical concentrations and the fill factors of the deconvoluted images are all approximately 10% compared with the ground truth. For the smallest poly-Si feature of 230 nm, the absolute error is approximately 25 nm.