Tilt series STEM simulation of a 25×25×25nm semiconductor with characteristic X-ray emission

A Multichannel Cross-Modal Fusion Framework for Electron Tomography

In this paper, we present a multichannel cross-modal fusion algorithm to combine two complementary modalities in electron tomography: X-ray spectroscopy and scanning transmission electron microscopy (STEM). The former reveals compositions with high elemental specificity but low signal-to-noise ratio (SNR), while the latter characterizes the structure with high SNR but little chemical information. We use a multivariate regression to build a cross-modal fusion framework for these two modalities to simultaneously achieve high elemental specificity and high SNR for a target element chosen from the sample under study. Specifically, we first compute three-dimensional tomograms from tilt-series datasets of X-ray and STEM using different reconstruction algorithms. Then, we generate many feature images from each tomogram. Finally, we adopt partial least squares regression to assess the connection between these feature images and the reconstruction of the target element. Based on the simulated and experimental datasets of semiconductor devices, we demonstrate that our algorithm can not only produce continuous edges, homogeneous foreground, and clean background in its element-specific reconstructions but also can more accurately preserve fine structures than state-of-the-art tomography techniques. Moreover, we show that it can deliver results with high fidelity even for X-ray datasets with limited tilts or low counts. This property is highly desired in the semiconductor industry where acquisition time and sample damage are essential.

Optimizing experimental parameters for the projection requirement in HAADF-STEM tomography

Tomographic reconstruction algorithms offer a means by which a tilt-series of transmission images can be combined to yield a three dimensional model of the specimen. Conventional reconstruction algorithms assume that the measured signal is a linear projection of some property, typically the density, of the material. Here we report the use of multislice simulations to investigate the extent to which this assumption is met in HAADF-STEM imaging. The use of simulations allows for a systematic survey of a range of materials and microscope parameters to inform optimal experimental design. Using this approach it is demonstrated that the imaging of amorphous materials is in good agreement with the projection assumption in most cases. Images of crystalline specimens taken along zone-axes are found to be poorly suited for conventional linear reconstruction algorithms due to channelling effects which produce enhanced intensities compared with off-axis images, and poor compliance with the projection requirement. Off-axis images are found to be suitable for reconstruction, though they do not strictly meet the linearity requirement in most cases. It is demonstrated that microscope parameters can be selected to yield improved compliance with the projection requirement.