Smart Microscopy: Feature Based Adaptive Sampling for Focused Ion Beam Scanning Electron MicroscopyTim Dahmen; Philipp Slusallek; Patrick Trampert; Frank Mücklich; Michael Engstler; Christoph Pauly; Niels de Jonge
In: Microscopy & Microanalysis - The Official M&M 2016 Proceedings. Microscopy & Microanalysis (M&M-2016), July 24-28, Columbus, OH, USA, Page 632, Vol. 22, No. Suppl 3, Cambridge University Press, 2016.
A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-posteriori image processing techniques were applied to remove the resulting noise. The technique was realized by scanning the sample twice. The first, quick scan used small pixel-dwell times to generate a first, noisy image using a low electron dose. This image was analyzed automatically, and a software algorithm generated a sparse pattern of regions of the image that require additional sampling. A second scan generated a sparse image of only these regions, but using a highly increased electron dose. By applying a selective low-pass filter and combining both datasets, a single image was generated. The resulting image exhibited a factor of ≈3 better SNR than an image acquired with uniform sampling on a Cartesian grid and the same total acquisition time. This result implies that the required electron dose (or acquisition time) for the adaptive scanning method is a factor of ten lower than for uniform scanning.