Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning

Abstract

Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.

FIB-SEM Three-Dimensional Tomography for Characterization of Carbon-Based Materials

“…Another way is to improve the resolution of SEM. A super asymmetric resolution of 3D imaging technique has been reported for nanoand mesoscale morphologies [64].…”

A neural lens for super-resolution biological imaging

“…While parametric modelling approaches have been applied in order to enhance medical imaging [8], recent work using deep learning approaches has also shown the potential for the application of neural networks (NNs) for enhancing image resolution. Examples for such an application include, a 4x upscaling on photographic images [9], optical microscopy (improving the resolution from 40x to 100x) [10], dental imaging [11], phase imaging [12], fluorescence microscopy [13], magnetic resonance imaging [14], SEM imaging [15,16], positronemission tomography [17], stochastic optical reconstruction microscopy [18], and ultrasound imaging [19]. NNs are also well-suited to the classification of objects in images, and accordingly the classification of biological, pollution and colloidal particles from images and scattering patterns has also been demonstration [20][21][22][23][24].…”

General Resolution Enhancement Method in Atomic Force Microscopy Using Deep Learning

“…For example, a DL model developed for scanning transmission electron microscopy (STEM) can automatically detect and classify the defect transformation . A super‐resolution model using DL can restore 3D morphologies of scanning electron microscopy equipped with a focused ion beam (FIB‐SEM) . For Synchrotron‐based X‐ray tomography, a DL model is also developed for increasing the resolution of the X‐ray signals .…”

“…[21][22][23] Especially for image processing, deep learning provides a strong, effective, and efficient capability to detect and classify the objects or features in the images, as well as to predict unknown information based on limited information. [25] For Synchrotron-based X-ray tomography, a DL model is also developed for increasing the resolution of the X-ray signals. For example, a DL model developed for scanning transmission electron microscopy (STEM) can automatically detect and classify the defect transformation.…”