A Bayesian approach to spectroscopic depth sectioning for locating dopant atoms

Locating dopants in 3D is of great interest as advanced materials and devices increasingly rely on control at atomic dimensions, and microscopy tools are constantly in development for this purpose. One potential tool is electron energy loss spectroscopy (EELS) depth sectioning, where a core-loss signal is collected as a function of electron probe defocus along an atomic column in scanning transmission electron microscopy. Here we revisit its prospects through simulation with particular attention to dose considerations. We discuss pitfalls of by-inspection interpretation of EELS depth sectioning and resolve them by comparing with simulated references in a Bayesian framework. While direct electron detectors are starting to enable energy-filtered momentum-resolved maps, we show that momentum resolution offers no real advantage for depth determination. We extend the Bayesian framework to infer the depths of multiple dopants along a column without simulating all possible doping concentrations and configurations. We further show that zero-loss depth sectioning is too sensitive to residual aberrations to usefully allow the application of our Bayesian framework.