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.

I. Introduction

Electron tomography is a powerful tool in materials science to characterize the complex three-dimensional (3D) structure of inorganic specimens on the nanoscale [1]. In transmission electron microscopy (TEM), the sample under study is exposed to an electron beam and tilted to obtain two-dimensional (2D) projection images at different angles. Several imaging modalities exist, e.g., bright-field TEM [2] and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) [3]. In tomography, these projections are called a tilt-series, from which we can reconstruct a volume representing the sample [1]. Since the intensity of HAADF-STEM scales with the atomic number of the element (, , depending on the inner and outer detector angles [4]), it can also indirectly reveal the compositional information of the sample. However, when the sample has elements with close atomic numbers, HAADF-STEM images may no longer be distinctive for these elements. To better understand more complex compositions, spectral imaging techniques like energy dispersive X-ray spectroscopy (EDS) [4] must be pursued.