Sparse sampling is a design paradigm for electron microscopes that may be advantageous for some applications. To collect large data sets quickly, we pioneered sparse sampling in SEM. Here, we describe using image content to implement meander scan in SEM to avoid time-wasting ‘carriage return’ dynamics. We also describe a beam dynamic model extracted directly from meander scan images enabling sparse meander sampling, which further reduces collection time. Extension to multi-beam SEM is discussed.

This presentation is part of Minisymposium “MS11 - Computational Imaging for Micro- and Nano-structures in Materials Science (2 parts)”
organized by: Brendt Wohlberg (Los Alamos National Laboratory) , Jeff Simmons (Air Force Research Laboratory) .

Authors:

Kurt Larson (Sandia National Laboratories, US Department of Energy)

Keywords:

compressive sensing, computed tomography, computer vision, image representation, nonlinear optimization, sparse sampling, statistical inverse estimation methods