title: Retrieving depth-direction information from TEM diffraction data under reciprocal-space sampling variation.
abstract: For full three-dimensional information retrieval from transmission electron microscope data, retrieving the third-dimension (beam-direction) information is an important challenge. Recently, we have developed an artificial-neural-network-based retrieval algorithm suitable for retrieving three-dimensional nanoscale crystal parameters like strain, including with noisy data (R.S. Pennington, W. Van den Broek, C.T. Koch, Phys. Rev. B 89 (20) (2014) 205409 [12]). In this work, we examine how reciprocal-space sampling conditions influence the retrieved crystal parameters, using crystal tilt as an example parameter, and demonstrate retrieval for 2.5 nm depth resolution. For noise-free data, we find that the total reciprocal-space area is the key parameter; however, when the data are noisy, the number of reciprocal-space points and the amount of noise are also influential. We also apply our algorithm to a simulated bent specimen, and recover the bending as expected. Guidelines for experimental applications are also discussed.
authors.affiliation: Institute for Experimental Physics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany. Electronic address: robert.pennington@uni-ulm.de.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Pennington
authors.affiliation: Institute for Experimental Physics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
authors.forename: Christoph T
authors.initials: CT
authors.lastname: Koch
keywords: Artificial neural networks; Convergent-beam electron diffraction; Dynamical electron scattering; Inverse problems; Three-dimensional characterization