Computer vision AC-STEM automated image analysis for 2D nanopore applications

Abstract

Transmission electron microscopy (TEM) has led to important discoveries in atomic imaging and as an atom-by-atom fabrication tool. Using electron beams, atomic structures can be patterned, annealed and crystallized, and nanopores can be drilled in thin membranes. We review current progress in TEM analysis and implement a computer vision nanopore-detection algorithm that achieves a 96% pixelwise precision in TEM images of nanopores in 2D membranes (WS2), and discuss parameter optimization including a variation on the traditional grid search and gradient ascent. Such nanopores have applications in ion detection, water filtration, and DNA sequencing, where ionic conductance through the pore should be concordant with its TEM-measured size. Standard computer vision methods have their advantages as they are intuitive and do not require extensive training data. For completeness, we briefly comment on related machine learning for 2D materials analysis and discuss relevant progress in these fields. Image analysis alongside TEM allows correlated fabrication and analysis done simultaneously in situ to engineer devices at the atomic scale.